Machines doi: 10.3390/machines14101123

Authors: Paraskevi Zacharia Konstantinos Botsis Konstantinos Moustris Constantinos Stergiou

Industrial production systems require intelligent maintenance solutions to minimize unplanned downtime, improve reliability, and support data-driven decision making. This study presents a machine learning framework for condition monitoring and fault classification in industrial production systems. The framework is evaluated using an AI4I-derived synthetic dataset comprising 10,000 production events characterized by operational sensor measurements and machine failure indicators. An exploratory analysis is first conducted to examine data distributions, failure patterns, and relationships among operational variables. Subsequently, four classification models (Logistic Regression, Random Forest, Histogram-Based Gradient Boosting, and a Multilayer Perceptron (MLP) neural network) are developed and comparatively evaluated. The results show that nonlinear models significantly outperform the Logistic Regression baseline, with Random Forest, Histogram-Based Gradient Boosting, and MLP achieving very high classification performance. The findings suggest that interactions among operational variables contribute substantially to the fault classification task and are more effectively captured by nonlinear learning approaches than by linear models using the original feature set. Overall, the study provides a benchmark-style comparative evaluation of representative machine learning classifiers for fault classification on an AI4I-derived synthetic dataset. The findings primarily illustrate classifier behavior under controlled synthetic conditions and provide a basis for future validation using real industrial data and operational maintenance environments.

Machines doi: 10.3390/machines14101122

Authors: Huazhen Yu Wen Zhao Andrea D’Ariano Ruifei An Peng Xu Anzheng Lai

Heavy-haul railways are mainly located in mountainous regions, where wheel–rail adhesion is susceptible to variations in rail-surface conditions, posing challenges to the cooperative operation control of heavy-haul group trains (HHGTs). This paper develops an adhesion-dependent Tube-based model predictive control (Tube-MPC) method for HHGTs by incorporating adhesion conditions into disturbance set construction, robust error Tube design, and nominal constraint tightening. A control-oriented longitudinal dynamics model retaining position-dependent gradient resistance is established to describe the motion of heavy-haul trains. Adhesion-dependent limits on traction and braking forces are imposed as input constraints, while adhesion uncertainty is modeled as a bounded equivalent acceleration disturbance arising from adhesion-induced force mismatch. An ancillary feedback controller is employed to regulate actual-nominal state deviations and ensure robust constraint satisfaction. Simulations are conducted using parameters of approximately 5000 t heavy-haul trains and a real railway gradient profile under dry, wet, and rainy or snowy rail-surface conditions. The results demonstrate that, when the actual disturbance remains within the design bound, the trajectories of all following trains remain within the error Tube. Compared with PID and standard MPC, the proposed approach improves speed coordination and spacing regulation, indicating its effectiveness for the cooperative operation of HHGTs under complex wheel–rail adhesion conditions.

Machines doi: 10.3390/machines14101121

Authors: George Chantoumakos Georgios Tsimiklis Angelos P. Markopoulos Angelos Amditis Fotios Konstantinidis

Comminution is the most energy-intensive stage of mineral processing, and microwave-assisted comminution (MAC) can reduce grinding energy by selectively heating microwave-absorbing minerals within transparent gangue, generating thermal microcracks that improve liberation. MAC performance, however, depends on the ore mineralogy and surface, which fixed-parameter operation cannot accommodate. An integrated mechatronic “intelligent crusher” is presented unifying actuation (microwave source, feed system, adjustable crusher geometry), sensing (thermal infrared and hyperspectral imaging, HSI), and control (offline reinforcement learning). HSI-derived mineralogical features and infrared thermal features form the state of a behavior-regularized actor–critic (BRAC) controller trained offline on logged operating data to adjust the power, exposure, feed rate, and crusher setting. A two-dimensional coupled electromagnetic–thermal–mechanical finite-element study underpins the process model. It is executed with temperature-independent dielectric properties in a single staggered coupling pass, and so calibrates the damage law qualitatively rather than predicting stress quantitatively. It reproduces cracking thresholds from the literature and shows that thermal gradients decay with exposure time as (1 + t/τ)−0.57, so that damage at a constant dose falls from 0.63 to 0.02 as exposure lengthens from 0.25 to 16 s. On this basis, the phenomenological damage law, which had been exposure-insensitive, is corrected. On the FEA-calibrated simulator, the BRAC policy reduces the mean size targeting error by 62% (1.43 to 0.54 mm) and the total specific energy by 4.2% (6.50 to 6.22 kWh/t), averaged over five training seeds, relative to fixed-parameter operation, outperforms rule-based and behavior-cloning baselines, and generalizes to a simulated ore batch excluded from the training. The framework establishes a validated control architecture for adaptive MAC ahead of three-dimensional model extension and experimental deployment.

Machines doi: 10.3390/machines14101120

Authors: Jian Zhao Tongtong Li Zelin Yang Shize Qin Jiaqi Duan Hao Zhang Yanbo Wang

Free-floating space manipulators are strongly coupled systems in which manipulator motion affects spacecraft base motion through momentum exchange, making simultaneous end-effector control and disturbance suppression challenging. This work investigates how an additional actuated prismatic degree of freedom influences whole-arm coordination in a 6R1P free-floating space manipulator. Compared with a fixed-length 6R configuration, the prismatic joint enlarges the feasible motion space and introduces an additional motion-allocation direction for full-pose tasks under generalized-Jacobian constraints. A proximal policy optimization (PPO)-based controller is developed for full-pose reaching with spacecraft-motion-aware objectives. Simulation results show that the 6R1P configuration improves reaching performance and reduces spacecraft reaction compared with the locked-prismatic 6R baseline. Trajectory-level dynamic reconstruction further reveals that the disturbance reduction is not caused by direct cancellation from the prismatic joint itself, but mainly by configuration-dependent redistribution of revolute-joint motions and enhanced mutual cancellation among their reaction contributions. These results demonstrate that telescopic redundancy provides a mechanism for coordinated motion allocation in free-floating manipulation, enabling learned policies to exploit additional degrees of freedom for improved task execution and reduced spacecraft disturbance.

Machines doi: 10.3390/machines14101119

Authors: Sangang Yao Yijie Li Haichao Cai Hongfan Yang

Accurate remaining useful life (RUL) prediction of rolling bearings is important for condition-based maintenance. This study proposes an interpretable rolling RUL prediction framework integrating adaptive multidomain degradation representation, prognostic-stage localization, and differential Gaussian process regression (Diff-GPR). A 48-dimensional feature pool is extracted from six vibration domains, and bearing-specific degradation-sensitive subsets are selected using monotonicity, trendability, robustness, and redundancy criteria. The retained features are directionally aligned, percentile-normalized, equally fused, and smoothed to construct a fusion virtual health indicator (Fusion-VHI). A two-stage localization strategy identifies sustained degradation onset and the subsequent prediction-ready point, after which Diff-GPR models multiscale degradation increments and recursively updates failure time and RUL. The framework was evaluated on all 15 bearings from the Xi’an Jiaotong University–Changxing Sumyoung Technology (XJTU-SY) dataset and all 17 bearings from the 2012 Prognostics and Health Management (PHM2012)/PRONOSTIA dataset. Full final rolling prediction coverage was achieved, with mean absolute error/root-mean-square error (MAE/RMSE) values of 6.33/7.26 min on XJTU-SY and 6.03/6.98 min on PHM2012, and overall values of 6.17/7.11 min. Representative 99% predictive intervals achieved 100% prediction-interval coverage probability, while signed-error analysis identified 13 early and 19 late final predictions with an overall mean signed error of +1.77 min.

Machines doi: 10.3390/machines14101118

Authors: Ramazan Hakkı Namlu Ahmed Abotoor Ahmad Wael Alshaer Zekai Murat Kılıç

The demand for miniature 316L stainless steel components in critical sectors is increasingly met by additive manufacturing (AM); however, due to the increasing applications of 316L in advanced manufacturing industries, there is a need to identify the machinability performance for 316L based on the fabrication techniques for addressing challenges. This study investigates the micro-machining performance of 316L material fabricated via Laser Powder Bed Fusion (LPBF), Laser-Wire Directed Energy Deposition (LW-DED), and conventional (wrought) methods. Results reveal wrought 316L exhibits superior machinability, yielding the lowest cutting forces, surface roughness, burr heights, and tool wear. Compared to the wrought baseline, average cutting forces increased by 10.5% for LPBF and 41.4% for LW-DED. Areal surface roughness deteriorated by 12.3% for LPBF and 52.1% for LW-DED. Average maximum down-milling burr heights were 35.6% and 48.8% higher for LPBF-316L and LW-DED-316L, respectively, than for wrought 316L. The observed ranking of machining responses was associated with the specific material conditions investigated. SEM observations and microhardness measurements indicate morphological and hardness differences among the specimens, which may have contributed to the measured differences in cutting force, surface roughness, burr formation, and qualitative tool-condition observations. As a result, process-induced specimen variations fundamentally govern the micro-machinability of AM 316L, offering critical insights for optimizing post-processing operations.

Machines doi: 10.3390/machines14101117

Authors: Jesús Eduardo Cortés Flores César Humberto Guzmán-Valdivia Andrés Blanco Ortega Arturo Abundez Pliego Enrique Alcudia-Zacarías Héctor Ramón Azcaray Rivera

Bilateral robotic rehabilitation has emerged as a technological approach for promoting coordinated upper-limb training after stroke. This state-of-the-art review critically analyzes bilateral upper-limb rehabilitation robots with emphasis on mechanical architecture, actuation and transmission, bilateral interaction modalities, control strategies, assistance modes, and validation evidence. A structured literature search covering 2010 to 8 July 2026 identified 141 records; 23 technology-related publications were retained for the state-of-the-art analysis, comprising 18 primary bilateral robotic studies and 5 supporting technical/contextual publications. The reviewed systems were organized according to a hierarchical framework distinguishing end-effector, exoskeleton, and hybrid architectures from simultaneous bilateral, master–slave/mirror-based, and cooperative bimanual interaction modalities. The evidence indicates that end-effector systems favor mechanical simplicity and adaptable workspaces, whereas exoskeletons provide more direct joint-level control at the cost of greater alignment and mechanical complexity. Control approaches increasingly incorporate impedance, admittance, assist-as-needed, and bio-signal-based strategies to improve compliant interaction and adapt assistance to user contribution. However, many advanced systems remain supported primarily by engineering validation or experiments involving healthy participants, while direct post-stroke clinical validation is comparatively limited. Future development should therefore prioritize clinically validated adaptive assistance, control strategies capable of accommodating asymmetric bilateral contribution, and safe, usable, and affordable systems suitable for clinical and home-based rehabilitation.

Machines doi: 10.3390/machines14101116

Authors: Yongzhuo Liu Jiangmei Zhang Haolin Liu Yongfa Mi

Robotic sorting of mixed rigid–deformable technical waste requires the selection of a graspable first target while minimizing disturbance to surrounding objects. This paper proposes a lightweight material-conditioned target-preselection method for cluttered RGB-D scenes. Object instances are detected using YOLO26n and segmented using SAM2-B, while their material categories are predicted using DPB-CNN. Depth-consistency filtering is subsequently applied to refine the candidate masks. Each candidate is evaluated according to geometric accessibility and local overlap, while visually inferred rigid-over-deformable relations are used to penalize or exclude deformable objects constrained by rigid-like objects. AnyGrasp generates a 6-DoF grasp pose using dense target points together with a downsampled workspace cloud retained for collision checking. Experiments were conducted on a UR5 platform using 40 predefined layouts covering four representative rigid–deformable interaction patterns, with three trials per method for each layout. The proposed method achieved a target selection accuracy (TSA) of 86.7%, a first-grasp success rate (FSR) of 80.8%, and a severe-disturbance rates (SDR) of 8.3%. After Holm correction, TSA was significantly higher than for both baselines, and SDR was significantly lower than for Native-AnyGrasp. The observed FSR improvements did not reach the corrected significance threshold. These results support improved first-grasp decision-making and reduced disturbance relative to Native-AnyGrasp in the evaluated technical-waste mock-up scenarios.

Machines doi: 10.3390/machines14101115

Authors: Jinyue Kang Dapeng Zhu Yuanyuan Wang

Cargo items at different longitudinal positions experience different local base excitations because of vehicle-body bounce and pitch, creating position-dependent demands on vibration isolation and support stroke. This study presents a response-guided equivalent-design framework that links critical-position identification to the selection of nonlinear support characteristics represented by an equivalent force model for X-type cargo supports. A coupled model comprising prescribed stochastic road inputs, linear tire stiffness and damping, a four-degree-of-freedom half-car subsystem, and five vertically supported cargo masses of 2000 kg each is established. Cargo acceleration, relative support displacement, interaction force, and energy-related indicators are evaluated. Under the nominal condition used for design-target extraction, P5 is identified as the critical position, with a dominant local-base frequency of 2.3994 Hz and an RMS-equivalent displacement amplitude of 15.339 mm. These response characteristics, together with prescribed design constraints, guide the selection of equivalent support properties. The support force law includes basic stiffness, delayed hardening, a displacement-activated limiting term, displacement-dependent damping, and regularized friction. In the nominal system-level comparison, the low-frequency-compliant X-type scheme reduced the maximum P5 stroke from 35.68 mm to 31.81 mm relative to the feasible low-frequency linear reference, accompanied by small increases in acceleration and interaction-force RMS. Under the investigated perturbed conditions, the grouped X-type configuration reduced the largest P5 stroke from 56.05 mm to 47.83 mm relative to the linear reference; however, it still exceeded the prescribed 40 mm limit. Further parameter optimization, independent benchmark comparison, and experimental validation are required before engineering feasibility can be established.

Machines doi: 10.3390/machines14101114

Authors: Weidong Liu Jialiang Wang Qiushi Zhang Jun Xing Changzheng Li

Efficient forward and inverse design of laminated non-pneumatic tires requires repeated evaluation of their load-bearing and tire–ground contact responses. The high-fidelity laminated beam–grounding analysis (LB-GA) formulation captures the coupled band and spoke mechanics but requires iterative solution of 18 differential equations with unknown regional boundaries. This study derives explicit reduced-order relations for vertical stiffness and average contact pressure by combining laminated curved-beam mechanics with double-sided compression-ring theory. Joint fitting to 500 high-fidelity LB-GA solutions yields the empirical screening criterion Ropt=N0.68n0.83>40. In an additional set of 5000 independently generated cases spanning the investigated design and material domain, 97.68% of the cases satisfying this criterion have a maximum response error no greater than 10%. The explicit relations are subsequently used as a domain-masked mechanics constraint in a multi-fidelity physics-informed neural network trained with independent high-fidelity labels. With 5×105 labels, the model gives stiffness and pressure NRMSEs of 4.47% and 4.68%, whereas a data-driven model using 5×106 labels gives 5.00% and 8.37%. Model-preparation time decreases from 220.1 to 32.5 h. Multiobjective inverse design, a newly manufactured-tire experiment, and six reconstructed finite-element designs produce validation errors below 10%. The framework therefore enables accurate tire design with substantially fewer high-fidelity numerical labels.

Machines doi: 10.3390/machines14101113

Authors: Alexey Savostin Kayrat Koshekov Amandyk Tuleshov Yerkebulan Tuleshov Magzhan Kanapiya Anatoliy Prosselkov

The deployment of autonomous predictive vibration-based diagnostics in Edge-IIoT requires balancing the cost of Micro-Electro-Mechanical System (MEMS) accelerometers against their metrological limitations. This study proposes a parametric Sensor Digital Twin (SDT) framework for virtual benchmarking of measurement chains at the hardware-software co-design stage. The SDT model emulates mechanical and electrical filtering, aliasing, noise, and quantization; its fidelity was validated experimentally using a physical ADXL345 sensor, with an RMS noise error of 8.56%. Virtual profiles of the commercial ADXL345 and ADXL357 sensors were generated using the reference NLN-EMP centrifugal pump dataset acquired with a Wilcoxon 786B-10 piezoelectric accelerometer. Their diagnostic performance was evaluated using eight diagnostic features and five heterogeneous machine-learning algorithms under interpolation, forward extrapolation, and backward extrapolation scenarios across fault severity levels. In the interpolation scenario, the ADXL345 and ADXL357 profiles achieved Macro F1-scores of 0.8999 and 0.8947, respectively, compared with 0.9567 for the reference measurement chain. In the forward extrapolation scenario, the ADXL357 profile achieved a Macro F1-score of 0.7171, compared with 0.6768 for the reference measurement chain. The results confirm that the SDT can support sensor hardware selection through virtual benchmarking, while the considered MEMS accelerometers provide comparable diagnostic performance in the evaluated scenarios when a representative training dataset is available.

Machines doi: 10.3390/machines14101112

Authors: Jingyi Liu Fuzhen Xing Yawei Wang Yizhou Li Fenlan Ou Qiongfeng Shi Bo Ning Guobiao Hu

Ocean Thermal Energy Conversion (OTEC) is a promising renewable energy technology for remote islands, offshore platforms, and maritime infrastructure, yet its commercialization is constrained by low cycle efficiency, high auxiliary energy demand, and the engineering challenges associated with large-scale seawater transport. This study presents the integration and experimental evaluation of a 20 kW class containerized OTEC prototype using R134a in a closed Rankine cycle. The working fluid circulates within the power-generation module, while warm surface seawater supplies the evaporator and cold deep seawater is pumped through an insulated intake pipe to the container-mounted condenser. The prototype integrates a radial-inflow turbine, stainless-steel heat exchangers, circulation pumps, and a programmable logic controller-based control system. Land-based commissioning tests, conducted at initial warm-to-cold-water temperature differences of approximately 20–25 °C, produced a peak electrical output of 11 kW in one run and approximately 5 kW for 11 min in another. Sea trials in the South China Sea recorded a peak electrical output of 16.4 kW and a cumulative power-generation duration of 4 h 47 min across separate runs. These results document the integration and short-duration operation of the prototype under offshore conditions and identify priorities for improved control and longer-duration testing.

Machines doi: 10.3390/machines14101111

Authors: Bingliang Ye Jiacheng Sang Xuefu Yu Zhaoming Guan Mengying Yan Tao Tang

To address the high cost, structural complexity, and poor horizontal transplanting performance of existing sweet potato seedling transplanting mechanisms, this study proposes a Fourier series-based kinematic synthesis method and designs a non-circular gear planetary transplanting mechanism, which is then validated through virtual simulation and prototype testing. Based on the agronomic requirements for horizontal transplanting, key positions including seedling pick-up, soil entry, and soil exit are identified. The complex vector method is applied to establish a 2R open-chain kinematic synthesis model with trajectory and posture error equations, from which the optimal mechanism parameters are derived. Kinematic modeling of the non-circular gear planetary train is conducted, and auxiliary design software is developed to generate the pitch curves of the non-circular gears. A cylindrical cam mechanism is designed for seedling gripping and release. Structural design and virtual prototyping are completed, and simulations verify the theoretical model. A physical prototype is manufactured for kinematic and field testing. The results demonstrate high consistency among the measured, simulated, and theoretical trajectories, with a maximum deviation of 0.6° (relative error of 0.83%) at the key posture positions. At rotational speeds of the transplanting mechanism of 30 r/min and 40 r/min, the average transplanting success rates reach 91.2% and 81.2%, respectively, while the planting depth, horizontal underground length, and plant spacing all satisfy the agronomic requirements, confirming the feasibility of the proposed design. This study provides a theoretical and technical foundation for the development of a high-performance sweet potato seedling transplanting mechanism.

Machines doi: 10.3390/machines14101110

Authors: Lorraine Ramaphala Pitshou N. Bokoro Wesley Doorsamy

This study investigates predictive maintenance for SCADA-controlled pump stations using multi-sensor data processing and hybrid machine-learning models. The conventional maintenance approaches adopted in practice remain reactive, with little provision for actual early warnings under real-world conditions such as noisy data, class imbalance, or varying sensor dynamics. A data-driven solution is proposed to predict pump tripping events using operational SCADA system data for early warning with useful lead times. The dataset, obtained from a water-utility SCADA system, contained missing values, heavy-tailed sensor distributions, and substantial class imbalance. The preprocessing strategy used time-aware imputation, winsorisation, and a sliding-window configuration informed by the characteristics of the SCADA data. Benchmark machine-learning models achieved PR-AUC values of approximately 0.55 or lower for trip-escalation prediction, highlighting the difficulty of predicting rare trip events directly from SCADA data. The proposed hierarchical GRU-based framework achieved PR-AUC values exceeding 0.80, demonstrating a substantial improvement in predictive performance while maintaining high precision and low false-alarm rates. In addition, a Remaining Useful Life (RUL) component was included to extend the system to support near-term risk forecasting. Even so, long-term forecasts remained uncertain, indicating that further model development is required.

Machines doi: 10.3390/machines14101109

Authors: Panagiotis Kyratsis Pierre Vella

Advanced manufacturing processes and technologies have transformed various industries by enabling more efficient, sustainable, and precise production methods [...]

Machines doi: 10.3390/machines14101108

Authors: Muhammed İşci

Vibrations generated during cutting in machining processes present a significant challenge, directly impacting surface quality, tool longevity, and processing efficiency. Accurate modeling of vibration behavior under varying cutting conditions is therefore essential for advancing the understanding of machining dynamics. In this research, triaxial vibration accelerations in the tool holder region during CNC milling of Al 6061 material were experimentally measured. The proposed framework focuses on the prediction of process-level time-domain acceleration responses rather than high-frequency chatter or tooth-passing vibration components. The experiments incorporated a range of spindle speeds, feed rates, and depths of cut. The resulting multi-axial acceleration time series were modeled using deep learning-based time series approaches to capture complex and nonlinear dynamics. Specifically, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures were developed, and their generalization capabilities were assessed using the Leave-One-Experiment-Out (LOEO) method. Comparative analyses employing root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics indicate that both models reliably predict multi-axis acceleration responses. Notably, the LSTM architecture demonstrates a more balanced learning performance for representing long-term dynamics. These findings provide an effective, practical approach to data-driven modeling of vibrations in CNC milling processes.

Machines doi: 10.3390/machines14101107

Authors: Wei Han Xiangyu Liu Xichao Su Bing Wan Fang Guo Changjiu Li

Offshore wind power substations serve as fixed bases for unmanned aerial vehicle logistics distribution. In the mixed-fleet scheduling of multiple unmanned aerial vehicle types, parking spot allocation, configuration decisions, and transfer timing are deeply coupled, rendering traditional scheduling algorithms ineffective for efficient solution. To address this, this paper takes the context of substations performing material delivery and inspection missions to surrounding wind turbines, maintenance vessels, and other offshore facilities. An optimization model is constructed that comprehensively considers group priority, aircraft type differentiation, and configuration transition factors, with objectives of minimizing total transfer time, number of configuration changes, and workload variance among transfer crews. An event-driven configurable greedy initial solution generation strategy is introduced, and an adaptive large neighborhood search algorithm is designed, incorporating domain-knowledge-driven destruction and repair operators, an adaptive weight mechanism, and a simulated annealing acceptance criterion. Experimental results demonstrate that the proposed algorithm achieves the optimal mean objective values across all six test cases, outperforming the conventional adaptive large neighborhood search algorithm by 7.0–7.9% and the genetic algorithm by 14.4–22.1%, while maintaining the lowest standard deviation. The algorithm exhibits robust stability and practical applicability, providing effective decision support for unmanned aerial vehicle logistics distribution scheduling at offshore wind power substations.

Machines doi: 10.3390/machines14101103

Authors: João Barros Francisco J. G. Silva Raul D. S. G. Campilho Naiara P. V. Sebbe Rúben D. F. S. Costa André F. V. Pedroso Arnaldo G. Pinto

Industrial automation systems operating within geometrically constrained manufacturing environments require engineering approaches capable of integrating functional requirements, mechanical design, numerical verification, and industrial implementation within a traceable development process. This study proposes and empirically evaluates a requirements-driven engineering framework through the development of a three-axis Cartesian manipulator for the automated simultaneous extraction of four injection-moulded runners. The framework extends beyond the sequential application of established engineering tools by introducing a requirements-to-evidence structure in which measurable industrial requirements govern architecture selection and detailed design, while numerical predictions, controlled experiments, and extended industrial monitoring provide backward verification of requirement compliance. Finite element analysis predicted a global maximum displacement of 0.4453 mm and a displacement of 0.29–0.33 mm in the functionally critical gripper region. The maximum equivalent von Mises stress was 8.8 MPa, corresponding to a minimum safety factor of 39. Experimental measurement by three-dimensional laser scanning, with a nominal resolution of 0.001 mm, indicated a maximum relative displacement of 0.37 mm in the gripper region. Comparison with the upper numerical prediction resulted in an absolute difference of 0.04 mm and a relative deviation of 10.8% with respect to the experimental value. Both the numerical and experimental displacements remained below the prescribed functional limit of 1.0 mm. A controlled validation campaign comprising 1000 consecutive operating cycles showed maximum axis-position deviations of 0.8, 0.7, and 0.5 mm along the X-, Y-, and Z-axes, respectively, within the specified ±2.0 mm requirement, while successful runner extraction was achieved in 99.6% of the cycles. Extended monitoring under regular production conditions covered 38,690 cycles over three months, during which the system achieved 99.1% operational availability, with no recorded collisions or operator safety incidents. These results demonstrate the practical applicability of the proposed framework under the investigated industrial conditions and provide traceable evidence linking initial requirements, engineering decisions, numerical predictions, experimental measurements, and long-term operational performance.

Machines doi: 10.3390/machines14101106

Authors: Xuhao Yue Feng Peng Zejian Deng Haoran Li Chuan Sun Hao Shi Haiming Sun

At autonomous intersections in mixed traffic, where Connected and Autonomous Vehicles (CAVs) coexist with Human-Driven Vehicles (HDVs), scheduling must remain effective even when a reliable explicit HDV turning intention is not available sufficiently early for the scheduling decision. This paper proposes a learning-based platoon scheduling method for this information-limited setting. CAVs and HDVs are organized into mixed platoons or an HDV group, and a state-augmentation function is designed to encode their temporal relationship while preserving the priority of uncontrollable HDVs. An enumeration (EN)-based expert demonstration mechanism is further integrated into the training process to provide high-quality experience. In the representative training run reported in the manuscript, the expert-assisted model achieved a peak average reward approximately 14% higher than the model trained without demonstrations. Across the tested demand cases, the learned scheduler also obtained travel-cost performance close to the EN benchmark. The scope of the method is explicitly limited to the modeled lane-keeping and sensing assumptions; robustness to random seeds, unexpected HDV maneuvers, communication delays, and additional safety metrics requires dedicated validation.

Machines doi: 10.3390/machines14101105

Authors: Mainak Mallick Seong-Geun Shin Hyuck-kee Lee Seung-Kyum Choi

Few-shot meta-learning supports rapid adaptation with limited labeled data, but remains sensitive to hyperparameters and support set quality. We propose Uncertainty-Decomposed Reliability-Gated Meta-Learning (UDRML). It selects support sets with low predictive entropy, combines diverse adapted models to estimate aleatoric and epistemic uncertainty through mutual information, and uses a reliability score to accept, flag, or reject predictions. We evaluate UDRML on cross-domain few-shot LiDAR fault classification using a synthetically augmented nuScenes dataset. At 10-shot, UDRML improves accuracy over MAML by 11.8 points. Gating provides a further 13.3-point gain on accepted predictions, while measured epistemic uncertainty is 78% lower than that of the strongest ensemble baseline, ECMP. The framework supports selective automation by accepting reliable predictions and directing uncertain cases to verification or a fallback.

Machines doi: 10.3390/machines14101104

Authors: Gerardo Peláez Pablo Izquierdo Gustavo Peláez Higinio Rubio

This work investigates multimode vibration suppression in quay-side container cranes by combining analytical modeling, nonlinear multibody simulation, robust Input Shaping, and experimental implementation. A linearized double-pendulum model preserving the main geometric and inertial properties of the spreader–ISO-container assembly is first derived to characterize its coupled dynamics, comprising a low-frequency global pendular mode and a higher-frequency relative ringing mode. The errors introduced by linearization are assessed against a higher-fidelity Simscape Multibody model over a wide range of operating configurations. The comparison shows that the analytical formulation provides sufficiently accurate estimates of the modal frequency ranges for robust shaper design. Two Unity Magnitude One-Hump shapers are therefore synthesized for the identified low- and high-frequency bands and subsequently convolved into a multimode bang–off–bang command. Sensitivity analysis confirms simultaneous attenuation of both target frequency ranges. Numerical validation on the nonlinear multibody model shows that the reduction in residual vibration remains above 90% over the investigated variations in hoisting length and ISO-container center-of-mass position. Finally, a PLC-based laboratory implementation demonstrates that Unity Magnitude shaping can be executed through timed command toggles without online convolution, supporting its practical implementation using conventional industrial control hardware.

Machines doi: 10.3390/machines14101102

Authors: Mehmet Erkan Kütük Sadettin Kapucu

This study presents a method for tuning input motion to suppress residual vibrations in a flexible link manipulator, with particular relevance to mechatronic positioning systems. The approach is based on a four-segment acceleration profile that combines ramp, cycloid, and ramped-versine trajectories, enabling effective vibration reduction over a wide range of travel times. Unlike the conventional continuous reference profiles considered in this study, the proposed method does not require the total motion duration to be an integer or half-integer multiple of the system’s natural period to suppress residual vibration. The motion consists of acceleration and deceleration phases, each divided into two subsections to ensure smooth transitions. The formulation is developed analytically for both undamped and underdamped systems and is validated through numerical simulations and experiments on a servo-driven mechatronic setup equipped with inertial measurement units. For the cases considered in the quantitative comparison, the proposed method reduced the dominant first-mode residual-vibration amplitude by approximately 97–100% in the simulations and 92–98% in the experiments relative to the corresponding untuned commands. Performance is evaluated using the percent residual vibration (PRV) metric, with comparisons to commonly used motion profiles, including trapezoidal, trigonometric, and cycloidal trajectories. The results indicate that substantial vibration reduction is achieved across different travel times, including cases where the motion duration is shorter than one natural period. Overall, the proposed method offers a practical and flexible alternative to conventional motion tuning techniques and is well suited for high-speed, precision-sensitive applications such as robotic manipulators and automated manufacturing systems.

Machines doi: 10.3390/machines14101101

Authors: Ayman Samy Abdel-Khalik Hassan T. Ali

DC-signal injection into the secondary xy subspace is commonly used in multiphase drives for online stator-resistance estimation and winding-condition monitoring because it can be decoupled from the fundamental torque-producing excitation. Under open-phase post-fault operation, however, the post-fault current constraints unavoidably couple part of this injected DC signal into the zero-sequence subspace. The resulting zero-sequence current can establish a stationary third-spatial-harmonic field and produce an additional speed-dependent braking torque. This paper experimentally and analytically investigates how this braking effect depends on winding connection and coil pitch in six-phase induction machines. Dual three-phase (D3P), asymmetrical six-phase (A6P), and symmetrical six-phase (S6P) connections are evaluated using two separate 1.1 kW reconfigurable prototypes of the same machine rating and principal geometry but with different stator coil pitches. Isolated rotational tests at several DC-injection levels are used to characterize the braking-torque–speed behavior. A combined rotational–standstill single-subspace characterization procedure is also proposed to obtain practical local estimates of the associated zero-sequence equivalent-circuit quantities. The results show that D3P is much less susceptible to the braking effect because of third-harmonic MMF cancellation, whereas A6P and S6P exhibit substantially higher braking torque. Moreover, 5/6 chording provides an effective passive means of suppressing this undesirable component.

Machines doi: 10.3390/machines14101099

Authors: Valerio Iovino Gerlando Frequente Giuseppe Blunda Massimo Caruso Giuseppe Schettino Rosario Miceli

This paper presents a fault diagnosis approach for a five-level Cascaded H-Bridge Multilevel Inverter (CHBMI) supplying an Interior Permanent Magnet Synchronous Motor (IPMSM). The method is based on a two-dimensional Convolutional Neural Network (2D CNN) designed to process voltage signals converted into image representations. In particular, the inverter voltage waveforms are transformed into grayscale images through a time-series reshaping procedure. This allows for the model to capture spatial patterns associated with different fault conditions, including both open-circuit and short-circuit faults. A multi-branch CNN architecture is adopted to simultaneously process multiple voltage signals, improving the ability to distinguish between fault types and locations. The proposed framework is evaluated on a dataset including 17 operating conditions under different speed and load profiles. The results confirm that the proposed approach provides accurate and reliable fault detection and is suitable for real-time diagnostic applications in multilevel inverter-based drive systems.

Machines doi: 10.3390/machines14101100

Authors: Basem E. Elnaghi Hala Samy Sayed Abdelhafez Mohamed M. Ismail Ahmed M. Shehata Ahmed M. Ismaiel

This article introduces an adaptive fuzzy logic controller with an anti-windup mechanism (AFLC-AW) for enhancing dynamic control of permanent magnet synchronous generator (PMSG)-based wind power plants (WPPs). The proposed controller imposes maximum power point tracking (MPPT), reactive power control, and DC-link voltage regulation while mitigating actuator saturation and improving transient behavior. AFLC-AW’s performance is compared with proportional integral (PI), PI with anti-windup (PI-AW), and adaptive fuzzy logic controllers (AFLCs). AFLC-AW mitigates oscillations and overshoots. PMSG based on WPP is emulated and modeled with the Matlab/Simulink 2026a software. An experimental study was conducted using the Dspace DS 1104 control board in order to verify the simulation results. Results demonstrate that AFLC-AW provides faster tracking, lower overshoot, improved damping, and higher steady-state accuracy under varying wind conditions. Rotor-speed tracking performance improves by 64.32%, 40.36%, and 32.63% compared with PI, PI-AW, and AFLC, respectively. The AFLC-AW reduces the mean of six IAEs by 16.16% compared to the PI-AW, 5.89% relative to the AFLC, and 22.34% in comparison with the conventional PI controller. These results confirm that AFLC-AW enhances tracking accuracy while providing a practical control solution for efficient energy extraction and stable grid integration of PMSG-based wind energy systems.

Machines doi: 10.3390/machines14101097

Authors: Xiqiao Wu Binbin Li Muhammad Saqlain Saeed Zhaoyang Fu Li Wang

The spatial asymmetry of the air-gap flux density in consequent-pole permanent magnet (CPPM) machines leads to large cogging torque (Tc) and abundant even-order harmonics in the back electromotive force (BEMF). Conventional suppression methods rely on iterative parameter optimization, which often compromises structural modularity and component commonality. To address this issue, this paper proposes a design strategy based on preselection of a conventional reference machine. The strategy mitigates these drawbacks at the topological level and avoids multi-parameter optimization. First, an analytical Tc model is established. It accounts for parameter uncertainties and unequal pole widths, and reveals that the fundamental period of Tc is always equal to one slot pitch. Second, an analytical total-flux model is developed to evaluate the effects of parameter optimization on the total flux and the flux contribution per unit volume of permanent magnet (PM) material. On this basis, reference-machine selection criteria are proposed. Stator slot skewing is used to reduce Tc, and full-pitch windings are used to suppress even-order harmonics. The designed CPPM machine inherits the stator components and rotor PMs of the reference machine. A non-magnetic shaft is used to reduce unipolar end leakage flux. Only the iron-pole arc width is optimized through a single-parameter sweep. Finite-element (FE) analysis results show that the optimized CPPM machine exhibits reduced Tc and suppressed even-order harmonics. Compared with the reference machine, the optimized machine uses half the PM material. Its output torque reaches 81.33% of the reference value; its torque per unit PM volume increases by 62.66%; and the cost share of rare-earth PM material decreases from 34.00% to 21.58%. Finally, prototypes of both the reference and the proposed CPPM machines are fabricated. Measurements of the end leakage flux at the shaft extension and the output torque validate the theoretical analysis and optimization results.

Machines doi: 10.3390/machines14101098

Authors: Francis Mafuta Kifumbi Guyh Dituba Ngoma Fouad Erchiqui Valery Tusambila Wadi

Turbine selection requires information on the operating range as well as peak efficiency. This paper extends previously documented bench-scale Francis, Pelton, and Cross-flow turbine data through a new analysis of CFD and laboratory performance curves. The original ANSYS CFX (2025) calculations used steady incompressible RANS, standard k–ε closure, and Frozen-Rotor coupling. The reported global comparison gives relative-deviation ranges of 1.13–9.96% for shaft power and 0.83–4.67% for hydraulic efficiency; geometry adjustments against the same benchmarks prevent treating this comparison as independent validation. The present analysis extracts plotted operating points and normalizes power and efficiency by their sampled maxima and speed by the corresponding peak locations. Approximate upper limits for retaining 90% of the sampled maximum efficiency occur at normalized speeds of 1.13, 1.43, and 1.06 for the Francis, Pelton, and Cross-flow cases, respectively. These describe the recorded operating paths, which have different discharges and varying heads, rather than universal turbine-family characteristics. Static-pressure plots identify locations for further investigation, but their incomplete pressure reference prevents quantitative cavitation-margin or vapor-volume assessment. The resulting qualitative selection workflow connects operating-speed compatibility and performance retention to site-specific hydraulic, generator, manufacturing, and maintenance checks.

Machines doi: 10.3390/machines14101096

Authors: Bogdan-Teodor Godea Daniel-Constantin Anghel Ana Gogorici Daniela-Monica Iordache Adriana-Gabriela Șchiopu Marian-Cătălin Ducu

Existing methods for storing and handling unirradiated nuclear fuel are primarily designed for facility-level applications or for transporting unirradiated fuel components and assemblies. In contrast, this study proposes a structured conceptual design approach for a compact storage system tailored to research institutes and laboratory settings, where operational workflows are typically discontinuous. The research employs Quality Function Deployment (QFD) and the Theory of Inventive Problem Solving (TRIZ) to develop a storage system for unirradiated fuel rods maintained in a controlled helium atmosphere. The proposed design addresses multiple functional and technical requirements, including mechanical protection, stable positioning, controlled handling, maintenance of the helium atmosphere, and leak-tightness. QFD was initially used to identify, organize, and prioritize these functional requirements, translating them into technical characteristics. The House of Quality framework established the relationship between requirements and technical characteristics, highlighting key design parameters and their interactions. Subsequently, TRIZ was applied to address technical contradictions identified through QFD, such as conflicts between structural strength and vessel mass, handling accessibility and storage efficiency, damping and structural stiffness, internal support modularity and leak-tightness. This methodology improves the traceability of design decisions and provides a systematic approach to developing efficient storage solutions for unirradiated nuclear fuel rods in research environments.

Machines doi: 10.3390/machines14101095

Authors: Haojie Huang Jiaqi Zhou Xuanbo Liu Ting Sun Haotuo Liu Caixu Yue

The wear conditions of cutting tools directly affect the processing quality of workpieces and the safety of machine tool operation. Therefore, precise image segmentation of the wear area is the core prerequisite for tool condition monitoring. Aiming at these problems, such as the loss of context information, insufficient spatial feature recognition and high edge missing rate in current tool wear segmentation methods, this paper proposes an improved semantic segmentation model called MSC-DeepLabv3+. This model deeply integrates the Mamba layer with the Atrous Spatial Pyramid Pooling module in the encoder, enhancing the multi-scale global context representation and making up for the shortcomings of traditional convolutional long-range feature association. We design a multi-level shallow feature fusion strategy, which combines the convolutional attention module to adaptively screen and enhance features to suppress the interference of complex textures on the tool surface. Meanwhile, a feature-aware adaptive upsampling module is constructed to dynamically focus on and weight the worn edges, alleviating the problems of high-frequency detail loss and edge blurring. Experiments indicate that the Dice of this model reaches 0.9560, and the segmentation accuracy is significantly better than similar semantic segmentation methods. Meanwhile, under complex cutting conditions, it possesses high precision, which has practical engineering value for the intelligent management of tool conditions in intelligent manufacturing scenarios.

Machines doi: 10.3390/machines14101094

Authors: Rongrong Peng

Wind-turbine fault diagnosis is limited by incomplete fault information in single-sensor signals and the poor adaptability of fixed graph structures to non-stationary operating responses. This study proposes an adaptive correlation spatio-temporal-frequency graph convolutional network (AC-STFGCN) to address these limitations in multi-source wind-turbine fault diagnosis. One branch uses one-dimensional convolutions to extract local time-domain features, while the other learns time–frequency representations based on the short-time Fourier transform (STFT). Vibration and acoustic features from these branches are fused and encoded using a bidirectional long short-term memory (Bi-LSTM) network. Local temporal segments are then represented as individual graph nodes. Feature correlations between nodes determine an adaptive, sample-specific adjacency matrix that captures non-Euclidean dependencies across temporal segments and multi-domain features. PoolGAT combines hierarchical graph pooling and graph attention to emphasize fault-relevant nodes and suppress redundant information. Experiments used multi-source signals collected from a wind-turbine drivetrain fault-simulation test rig. AC-STFGCN achieved 99.08% accuracy, 99.12% precision, 99.03% recall, and an F1-score of 99.07%. Confusion matrices, receiver operating characteristic (ROC) curves, and t-distributed stochastic neighbor embedding (t-SNE) visualizations also showed improved discrimination between similar fault patterns. Further experiments at load levels of 0%, 25%, 50%, 75%, and 100% assessed the sensitivity of AC-STFGCN to load-dependent signal variations. All four diagnostic metrics remained above 98% across the evaluated load range.

Machines doi: 10.3390/machines14101093

Authors: Mithat Önder Muhsin Uğur Doğan

This paper presents an Internet-of-Things (IoT) prototype that uses a single six-axis MEMS inertial sensor mounted on the motor housing to monitor three-phase induction motors, estimate their operating frequency, and diagnose phase-loss faults. The sensor data is acquired by an STM32F103-based node and transmitted simultaneously via Bluetooth and Wi-Fi to an Android application, where the motor condition, estimated operating point, and vibration level are displayed. This approach eliminates the additional cost and wiring typically required for current sensors, tachometers, and industrial accelerometers. Both datasets were collected at 150 Hz using all six inertial channels, with five independent trials conducted for each condition. The sensor was removed and remounted between trials. The drive-setpoint dataset comprised 270,000 samples collected at twelve setpoints ranging from 5 to 60 Hz, while the phase-loss dataset contained 45,000 samples labelled as healthy or as one of three single-phase-loss faults. Die temperature was recorded as a seventh channel but was not used in any model. Windowed time- and frequency-domain features were compared with raw-sample inputs using six classical machine learning model families and four deep learning architectures. The evaluation included a buffered chronological split, blocked five-fold cross-validation, and leave-one-trial-out cross-validation. In the final protocol, neither the held-out recording nor data from the corresponding sensor mounting were included in training. The repeated trials made this evaluation possible, and all headline results are reported under this protocol. When evaluated on held-out trials, four-class phase-loss diagnosis achieved 96.2% accuracy across 450 windows, with a cluster-bootstrap interval of 89.0–99.8% calculated over the twenty held-out recordings. The commanded drive setpoint was estimated with an RMSE of 1.50 Hz for setpoints spaced 5 Hz apart. Because a four-class score averages two tasks of very different difficulty, the two are also scored separately: detecting a fault transfers exactly, at 450 out of 450 windows for four of the five models, while naming which of the three phases was lost falls from 99.1–100% within recordings to 86.2–96.9% across them. The within-recording protocols report 100% and 0.36 Hz on the same data, and that gap is the central methodological result: a leakage-safe split inside a recording is not a substitute for a held-out acquisition. Diagnostic tests clarify the limits of the system. Accelerometer amplitude consistently tracked the drive setpoint across five sensor remountings and remained informative after accounting for temperature, indicating that the model estimates the setpoint from signal level rather than directly measuring frequency. The accelerometer carried more relevant information than the gyroscope. Because setpoint estimates became unreliable during faults, the application now suppresses them whenever a fault is detected. The system is therefore presented as a feasibility prototype

Machines doi: 10.3390/machines14101092

Authors: Haoran Wang Yueling Zhang Qiming Men Jiale Gu Putai Zhang Sun Jin Quan Zhou Mian Li

High-temperature sodium heat pipes are promising passive heat-transfer components, yet numerical redesign is often disconnected from manufactured and power-tested hardware. This study develops a reference-anchored constrained bi-objective optimization framework based on the common nominal configuration shared by 139 manufactured and power-tested sodium heat pipes at 700 °C and a 4 kW total heat load. Five structural variables determine total mass and thermal resistance under a fixed rated cooling boundary, while eight nonlinear constraints represent wick geometry, five heat-transfer limits, hoop stress, and guide-contact/heat-leak feasibility. A staged workflow separates lexicographic sequential quadratic programming (SQP) endpoint construction, genetic algorithm (GA)-based global candidate generation, augmented-Tchebycheff scalarization, multistart SQP refinement, and independent ε-constraint verification. The scan retained 134 of 420 designs as feasible. The minimum-mass and minimum-resistance endpoints were 1.4837 kg at 0.1280 K/W and 0.0699 K/W at 2.4528 kg, respectively. The endpoint-seeded original GA produced 40 nondominated points, whereas the proposed hybrid method produced 119 nondominated points and recovered 23 of 27 unsupported points identified by the dense ε-constraint reference. The framework delivers numerical design candidates for prototype manufacture and rated power testing; its engineering value is to narrow prototype choices, identify constraint-controlled trade-offs, and connect batch-tested hardware with subsequent redesign and power-test validation.

Machines doi: 10.3390/machines14101091

Authors: Xiangdong Yu Wuchao Chen Zongpeng Tong Shuai Dong Haiyan Qiang

As core components of marine power systems, sliding bearings directly affect the operational safety of ships. To address the challenges that early weak anomaly features of sliding bearings are easily masked by electromagnetic interference (EMI) and that a single evaluation indicator may respond differently across wear conditions, this paper proposes an asymmetric autoencoder dual-indicator anomaly detection (AADI-AD) method. First, adaptive notch filtering and high-pass filtering are employed to suppress EMI. Then, horizontal and vertical asymmetric convolution kernels are introduced, and an autoencoder is constructed in combination with a dynamic feature allocation mechanism to represent directional time–frequency structures. Finally, a dual-indicator diagnostic plane is established by combining time-domain kurtosis with the mean squared error (MSE) for time–frequency image reconstruction. The experimental results show that the two indicators provide complementary responses across the examined wear conditions and reduce missed detections compared with single-MSE evaluation. These results demonstrate the effectiveness of the proposed method for sliding-bearing anomaly detection under the EMI-contaminated conditions represented by the present experimental platform.

Machines doi: 10.3390/machines14101090

Authors: Armands Sīlītis Leonids Ribickis

Slotless permanent magnet synchronous motors are attractive for precision electric-drive applications because eliminating stator slots can suppress slotting-related torque disturbances; however, their analytical performance relationships require experimental verification under realistic drive conditions. This study experimentally validates the dynamic characteristics of a 1.7 kW, 10-pole slotless permanent magnet synchronous motor (PMSM) and evaluates its measured performance against a theoretical conventional PMSM benchmark. A dedicated laboratory test bench was used for multi-step, no-load speed tests and rated-speed load tests with synchronized acquisition of rotational speed, stator current, torque, drive-reported voltage, and mechanical power. At approximately 2000 rpm, the current-torque data were well described by I_s = 0.668T + 0.208 A (R2 = 0.9975); the coefficient of determination is used only as a statistical goodness-of-fit measure. Mechanical power reconstructed from P_m = Tω agreed with the torque-sensor power channel within approximately 0.04% mean absolute relative difference, confirming internal consistency of the mechanical data. At four matched torque points, the slotless prototype required 7.87% less current on average than the linear reference and exhibited an 8.70% higher effective torque-to-current ratio. Under an equal-resistance sensitivity assumption, the corresponding normalized current-squared indicator was 14.99% lower, on average. The results validate the proposed analytical-to-experimental method within the investigated operating range while explicitly identifying the benchmark and measurement limitations.

Machines doi: 10.3390/machines14091089

Authors: Yingqi Xiao Jingang Wang Jialong Chen Haiming Zhao Gang Hu

Road-induced vibration of vehicle-mounted laser platforms is stochastic, and the structural response is influenced by support motion, damping, and connection stiffness. This study combines road measurements with finite element (FE) random-vibration analysis to assess the response of a vehicle-mounted laser platform. Two screw-mounted tri-axial accelerometers measured the excitation at the left-wheel support and the response at the laser-head mount during three repeated runs on each of three roads. The records were sampled at 200 Hz for 33.305 s and processed using 1024-sample Hann-windowed Welch estimates with 50% overlap, corresponding to a frequency resolution of 0.1953125 Hz. A three-level modal mesh study supported the selection of an 8 mm global mesh. The random-vibration solution included 20 modes up to 78.911 Hz, while six selected modes were displayed in post-processing. For the three road conditions, the mean absolute error in the three-axis resultant RMS was approximately 4.85%, and the mean logarithmic PSD correlation coefficient over 0–80 Hz was approximately 0.979. The results support comparative response analysis for the tested configuration. The 2% damping ratio and equivalent support stiffnesses were treated as modeling assumptions, and the single measured input required an equivalent uniform-support excitation model.

Machines doi: 10.3390/machines14091088

Authors: Torpat Sirilattaporn Nataporn Luksiri Worathris Chungsangsatiporn Gridsada Phanomchoeng Ratchatin Chancharoen

This study presents a programmable pressure pneumatic knife CNC platform for the fabrication and experimental investigation of flexible sheet materials. The platform enables independent control of blade-loading pressure, feed rate, and toolpath motion, allowing pressure–feed rate operating windows for mechanical knife cutting to be established. Paper and vinyl sheets are evaluated using square and straight-line cutting patterns, with cutting quality classified into four regimes: clean cut, skimming, tearing, and crushing. The results demonstrate that insufficient pressure causes unstable blade–material contact, resulting in skimming or no-touch behavior, whereas excessive pressure promotes tearing. Paper attains a 99% clean cut rate at 1 bar and 9000 mm min−1 during square cutting, while vinyl exhibits 100% clean cuts across 3000 mm min−1 to 9000 mm min−1 at 3 bar. Straight-line cutting achieves clean cut rates of 98% for paper and 97% for vinyl at 2 bar and 7000 mm min−1. These findings support the effectiveness of programmable pressure mechanical knife cutting for repeatable fabrication of flexible sheet materials.

Machines doi: 10.3390/machines14091087

Authors: Fanbin Meng Cheng Chen Huixin Yang

During the early developmental stages of solid rocket motors (SRMs), particularly under extreme operating conditions, the scarcity of experimental data severely limits the predictive capabilities of conventional deep learning models. To address this challenge, this paper proposes a novel hybrid predictive framework, termed Shape-Regularized Meta-Variational Multi-Scale Network (SR-MVSNet), tailored for few-shot thrust prediction via shape-regularized meta-learning. First, a variational autoencoder (VAE) maps nine static design and operating parameters to a latent probability distribution and reconstructs the parameter vector. A parallel multi-scale convolutional neural network (MSCNN) then processes the reconstructed features to predict the complete thrust curve. Crucially, a joint loss function with shape regularization is integrated within the model-agnostic meta-learning (MAML) architecture, guiding the network to reproduce the measured thrust build-up and decay through supervised first- and second-order difference matching. Experimental results demonstrate that the proposed framework achieves the lowest mean squared error among the evaluated models in the low-temperature-to-ambient-temperature transfer task. Notably, during the critical steady-state combustion phase, the mean absolute percentage error is 1.71% under the combined-source task, supporting accurate steady-state thrust prediction for the rapid performance evaluation of SRMs in engineering applications.

Machines doi: 10.3390/machines14091086

Authors: Mei Ming Shengnan Li Yumei Ai Yandan Liang Shengtian Sang Haifeng Zhang

Rotating machinery sits at the heart of petrochemical, power generation, and other heavy industries. Its reliability directly shapes production output and cost. Infrared thermography enables non-contact, real-time monitoring of equipment surface temperatures. This makes it a useful tool for early fault detection. Yet images captured in factories typically carry mixed noise from sensors, transmission, and the environment. Such noise often obscures the thermal signatures that indicate faults. This paper puts forward a complete edge-deployable pipeline that denoises infrared images and then diagnoses faults. The first stage is a denoising method built on the stationary wavelet transform (SWT) and a parallel dual-branch U-Net. Under mild, moderate, and severe mixed noise, it reaches 40.99, 35.08, and 31.76 dB PSNR, respectively. The second stage is a lightweight CNN-Transformer-CBAM classifier. It uses depthwise separable convolution and a CBAM attention module. On an 11-class induction motor dataset, it scores 98.67% accuracy (five-run average). The model also achieves 98.75% on a 9-class transformer dataset and 99.50% on a 5-class pump dataset, demonstrating cross-device generalization. The model occupies only 2.758 MB. After INT8 quantization, it runs on the RK3588S edge board. Average CPU inference takes 103.026 ms. The 99th-percentile latency stays under 124.778 ms. These numbers support the feasibility of factory-floor monitoring.

Machines doi: 10.3390/machines14091085

Authors: Sheng Liu Xiaolong Wei Pei Feng Xiaoping Cui Dayong Jiang Jiakai Liu Zhoubo Wang

Airborne capture net devices mounted on unmanned aerial vehicles (UAVs) provide an effective physical countermeasure against rogue drones. However, conventional deterministic designs neglect parameter uncertainties such as manufacturing tolerances and assembly errors that degrade flexible net deployment dynamics. To resolve this, this study proposes a multi-objective robust design optimization framework integrating a feedforward neural network (FNN) with uncertainty quantification. A lumped-mass dynamic model is formulated; static deployment experiments validate its high fidelity during the initial phase (t ≤ 0.08 s), while subsequent full-deployment and contraction phases are captured via experimentally calibrated numerical extrapolation. To mitigate computational costs, an adaptive FNN surrogate model is developed, reducing single-evaluation times from hours to seconds. Monte Carlo simulations coupled with the non-dominated sorting genetic algorithm II (NSGA-II) are then employed to optimize the mean and standard deviation of deployment and effective distances. Compared with deterministic optimization, the robust solution yields a 23.8% increase in the 95th percentile of deployment distance with only a 1.5% decrease in effective distance. Crucially, the standard deviations of these metrics decrease by 22.5% and 38.5%, respectively, demonstrating significantly attenuated sensitivity to parameter variations and offering a valuable engineering framework for robust airborne interception.

Machines doi: 10.3390/machines14091083

Authors: Feiyan Guo Zhenhao Wang Yanfu Dong

The linear axis is a core motion module in precision equipment and directly affects assembly and machining accuracy. Inaccurate prediction of motion error may lead to failure in aerospace manufacturing and assembly. Most existing methods typically assume rigid components and neglect tolerance coupling, yielding inaccurate predictions. Therefore, a method considering both tolerance coupling and worktable elastic deformation is developed. First, the variation ranges of geometric errors under the coupled tolerances were characterized using Small Displacement Torsor theory. Second, a two-stage error propagation model is established: errors were initially propagated from the base to four sliders by Homogeneous Transformation Matrices, and subsequently mapped to the worktable utilizing transfer coefficients derived from finite element analysis. Finally, Monte Carlo Simulation was employed to obtain the error variation intervals and their statistical distributions. A case study demonstrated that neglecting worktable elastic deformation underestimates translational errors by up to 40%. Meanwhile, neglecting tolerance coupling would underestimate rotational and translational errors by up to 43% and 25%, respectively. Furthermore, applying the proposed model to tolerance allocation proved that the schemes guided by simplified models would cause critical design failures. Therefore, incorporating both factors is important for obtaining physically grounded error predictions and for improving the reliability of tolerance allocation.

Machines doi: 10.3390/machines14091084

Authors: Chenyu Wu Ziwen Wu Jianxiong Gao Yiping Yuan

Wind turbine main bearings are critical components in the drivetrain and are characterised by long service life, low failure rates, and limited failure-interval samples, which increases uncertainty in reliability assessment and maintenance decision-making. To improve the utilisation of limited failure-interval information in small-sample reliability modelling, this study develops a unified three-way expansion Bootstrap strategy combined with a three-parameter Weibull distribution. The principal methodological contribution lies in integrating intra-interval supplementary sampling, left-boundary expansion, and right-boundary expansion within the same sample-generation framework, thereby enabling the main distributional information and boundary information contained in the available failure-interval samples to be utilised jointly. Based on 36 equivalent failure-interval samples obtained from Romax fatigue-life simulations under different operating conditions, the proposed method is compared with traditional Bootstrap and two-way expansion Bootstrap methods. The results of the two-sample K-S test indicated that no statistically significant distributional difference was detected between the expanded samples and the original sample. Using the three-parameter Weibull fitting results obtained from the original 36-sample dataset as the reference, the proposed three-way expansion method yields the smallest relative deviation of the scale parameter η among the three expansion strategies, at 2.69%. The MTBF relative deviations of the traditional Bootstrap, two-way expansion Bootstrap, and three-way expansion Bootstrap methods were 4.81%, 7.36%, and 7.76%, respectively. Repeated simulation results further show that the three-way expansion method provides substantially lower MTBF variability than the traditional Bootstrap method, although the two-way expansion method yielded the smallest MTBF standard deviation. The results demonstrate the methodological potential of the proposed strategy for small-sample MTBF modelling of wind turbine main bearings under the investigated simulation conditions.

Machines doi: 10.3390/machines14091082

Authors: Chao Yang Chenbo Yin Shoulei Ma Wei Ma Hao Feng Donghui Cao

The return oil filter in an excavator hydraulic system plays a critical role in maintaining oil cleanliness, and its blockage fault directly affects the thermal state and operational reliability of the machine. Owing to the progressive evolution and concealed characteristics of this fault, accurate identification remains challenging. To address this issue, this study proposes a return oil filter blockage fault identification model combining the whale optimization algorithm (WOA), multilayer perceptron (MLP), and gated recurrent unit (GRU). First, the hydraulic system configuration, operating process, and blockage mechanism of the return oil filter are analyzed. Then, multi-source operational variables are selected according to the fault propagation mechanism, while ACF and FFT analyses are employed to support signal denoising. In addition, variational mode decomposition (VMD) is introduced for representative pressure signals to provide supplementary evidence for fault evolution and the rationality of variable selection. On this basis, WOA is used to optimize the key hyperparameters of the hybrid model. Finally, the proposed scheme is systematically validated. Experimental results show that WOA-MLP-GRU achieves the best overall performance among the compared models, with an identification accuracy of 98.3%. Moreover, the proposed model exhibits lower validation loss, better feature separability, and a more concentrated error distribution, demonstrating superior training stability, robustness, and generalization capability.

Machines doi: 10.3390/machines14091081

Authors: Zhiao Cheng Qianqian Zhang Zerui Li

Intelligent navigation of mobile robots in unknown environments has become a key enabling technology for service and logistics applications. However, in unstructured scenarios, perception noise and environmental uncertainty can significantly degrade system performance, making it a major challenge to balance safety and efficiency. In this work, we propose a Human–Machine Shared Control method with Model Predictive Control constraints (HMSC). HMSC establishes a confidence-driven human–machine shared control mechanism that maximizes collaborative efficiency by dynamically assessing the reliability of agent decisions to regulate control weights. Simultaneously, the method introduces a composite confidence evaluation model which, by fusing epistemic uncertainty with geometric feasibility from lightweight LiDAR measurements, achieves a robust quantification of policy risk. To ensure safe execution, we develop a multi-trajectory prediction mechanism which, after validating kinematic constraints, minimally intervenes to safely adjust control commands. We conducted Gazebo-based simulation experiments on obstacle avoidance and target navigation using a LiDAR-equipped mobile robot model and validated the rationality of the confidence model. The results demonstrate that the proposed shared control strategy, which combines confidence assessment with deterministic safety boundaries, significantly improves the success rate and robustness of the system in uncertain environments.

Machines doi: 10.3390/machines14091080

Authors: Weiwei Hu Ruiqin Li Di Lin

Based on the advantages of redundancy, high stiffness, and strong load-bearing capacity of parallel mechanisms, they can be applied in the field of mobile robots. This paper proposes a novel three-platform parallel-coupled mobile robot and conducts motion analysis and dynamic modeling for it. It proposes an active internal force regulation strategy and a driving force synchronization and coordination control strategy based on the idea of collaborative integration, and optimizes these two control strategies using neural network methods. Finally, prototype experimental tests show that compared with force–position hybrid control, the active internal force regulation reduces internal force error to 11.6% of the initial value, which is further reduced to 6.5% after neural network optimization; the synchronization–coordination control cuts branch driving error to 10.2% of the initial value and further to 5.1% via neural network optimization. Adaptive compensation for residual coupling and model uncertainty is achieved, further realizing high-precision convergence of internal force errors. This provides a reliable internal force control guarantee for stable collaboration and high-precision operation of robots.

Machines doi: 10.3390/machines14091079

Authors: Qian Qiao Dawei Guo Lap-Mou Tam

Drill wear monitoring is essential for maintaining hole quality and avoiding unexpected tool failure, yet it remains challenging because of the enclosed cutting zone in drilling. This study presents an in situ monitoring method based on a wireless BT40 instrumented toolholder that synchronously measures axial force, torque, and two orthogonal bending moments during continuous drilling of 304 stainless steel with an 8.5 mm cemented-carbide drill. Tool wear progression, represented by the number of drilled holes, was correlated with drill morphology, SEM observations of the hole wall, and surface topography measurements. The results show that axial force reflects the increase in overall cutting resistance, torque provides information on torsional fluctuation, and the two bending moments are the most wear-sensitive signals because they capture radial load imbalance and asymmetric tool–workpiece interaction. As drilling progressed from the 10th to the 60th hole, bending-moment features increased markedly, while hole-wall roughness deteriorated from Ra = 0.589 μm to 2.528 μm and Rq from 0.760 μm to 2.916 μm. These findings indicate that the proposed toolholder provides physically interpretable features for drill-wear identification and tool-change warning.

Machines doi: 10.3390/machines14091078

Authors: Fuyou Li Jianxin Zhu Wanrong Wu Xiangfu Zou Zhanhao Ma

General aviation has experienced sustained global growth, yet landing safety attracts significant attention, with nearly 60% of accidents occurring during ground operations and take-off or landing phases. The expanding operational scenarios of general aviation towards unpaved and non-standard airfields have led to an increasing demand for high-performance landing gear shock absorption systems. Mechanical variable stiffness and damping serves as a conventional and mature solution, but it cannot maintain optimal performance across the full range of varying landing and taxiing conditions. Adaptive stiffness and damping technologies offer a promising R&D direction to overcome the limitations of traditional landing gear designs. However, no previous review has examined both their mechanisms and control strategies together from the general aviation perspective. This review follows the PRISMA methodology to propose a hierarchical assessment framework for variable stiffness and damping mechanisms in general aviation landing gear, covering the representative spectrum from conventional passive mechanisms to adaptive solutions. The potentially transferable concepts from other aircraft categories and non-aviation domains are also identified to strengthen and broaden the technical pathways, and their suitability for general aviation aircraft is critically assessed. Adaptive control strategies addressing three categories of external uncertainties are reviewed and compared, namely uncertain sink speed and landing weight, uncertain landing attitude, and uneven operating surfaces. Technology readiness, airworthiness requirements, and practical implementation concerns are systematically discussed, revealing notable challenges in transitioning from laboratory research to engineering applications. Finally, a three-term roadmap is proposed to outline the future research priorities, including hardware maturity enhancement, networked cooperative control, and AI-enabled autonomous adaptation. The findings presented herein provide an integrated reference for researchers and industry practitioners and are intended to foster the advancement of adaptive shock absorption systems for general aviation aircraft landing gear.

Machines doi: 10.3390/machines14091077

Authors: Nurtilek Sagynbayev Cristian Copilusi Prashant Jamwal Nursultan Zhetenbayev Yerkebulan Nurgizat Aidos Sultan Kassymbek Ozhikenov Gani Sergazin

Children with cerebral palsy often experience gait impairments that reduce mobility and functional independence. This study presents a preliminary computational comparison of four manually developed PEEK-based hip-joint configurations intended for a pediatric lower-limb rehabilitation exoskeleton. A planar kinematic-static model was used to calculate reaction forces and hip-joint moments for equivalent static load-direction angles ranging from 0° to 44°. The four configurations were then evaluated by finite element analysis using a homogeneous, isotropic, and linearly elastic PEEK model. The calculated hip-joint moment decreased from 392.66 N·m at 0° to 153.13 N·m at 44°. Under the highest-moment static case, Model 3 produced the lowest resultant displacement of 2.992 mm, whereas Model 4 produced the lowest calculated maximum von Mises stress and equivalent strain, with values of 100.1 MPa and 0.01509, respectively. The stress calculated for Model 4 corresponded to a yield ratio of approximately 1.10 relative to the assumed PEEK yield strength of 110 MPa. This small numerical margin does not establish structural safety. The results are limited by the absence of mesh-convergence verification, dynamic and CP-specific loading, manufacturing anisotropy, fatigue analysis, and experimental validation. Model 4 is therefore reported only as the configuration with the lowest calculated peak stress under the adopted numerical conditions. Further numerical verification and mechanical testing are required before practical pediatric use can be considered.

Machines doi: 10.3390/machines14091076

Authors: Yi Yang Lai Wei Changning Liu

Driven by the dual trends of electrification and intelligent technologies, multiple domains—including automobiles, railway, motorcycles, agricultural machinery, vertical takeoff and landing aircraft, and engineering vehicles—have undergone extensive electrification [...]

Machines doi: 10.3390/machines14091075

Authors: Xutao Lu Xingpeng An Jing Li Minghuan He Hao Liu

In complex industrial environments, vibration and acoustic signals are often corrupted by strong noise, resulting in non-stationary and low signal-to-noise ratio (SNR) characteristics that hinder effective feature extraction and degrade model generalization under limited samples. This study proposes a frequency-enhanced multi-scale adaptive state space Mamba (MSA-Mamba) model for motor fault diagnosis. A CNN Stem is first introduced for local feature extraction and sequence compression, followed by an adaptive frequency-domain filtering module to suppress noise and enhance fault-related frequency components. An MSA-Mamba Block combining multi-scale convolutional attention and parallel state-space modeling is then developed for adaptive feature fusion. In addition, Mixup and label smoothing strategies are employed to improve small-sample generalization. Experimental results show that MSA-Mamba achieves accuracies of 88.28%, 98.44%, and 99.48% on single-, three-, and four-channel datasets, respectively, and maintains 91.41% accuracy under strong-noise conditions. The proposed method demonstrates superior robustness and generalization for motor fault diagnosis.

Machines doi: 10.3390/machines14091074

Authors: Qingsong Zhang Shaoyang Liu Jinbao Chen

For the wingtip-connected combined UAV considered here, the proposed constraint-based assessment demonstrates that the clearance margin changes sign between the sampled 5° and 6° angles of attack, thereby bracketing the clearance transition within this interval. This study presents a deterministic, constraint-based assessment framework for the separation of wingtip-connected combined unmanned aerial vehicles (UAVs). The previously developed torque-driven compliant interface is treated as the existing physical platform rather than as a new mechanism contribution. Structural-strength, roll-control, and collision-clearance requirements are formulated as individual limit-state margins and linked by a non-compensatory minimum operator, so that failure of one quantified constraint cannot be offset by favorable performance in another. Previously reported aerodynamic, finite-element, multibody-dynamics, and ground-test records are reanalyzed as case-study inputs; they are not presented as independent validation of the complete classifier. The verified stress contours show that parametric refinement reduces the maximum equivalent von Mises stress from 17.2 MPa to 10.4 MPa (39.5%). Ground measurements acquired at 1000 Hz yield R2 = 0.96 for a descriptive sinusoidal fit, supporting response smoothness but not proving the complete low-impact safety hypothesis. The framework therefore provides a traceable requirement-checking route; with the presently retained records, its demonstrated implementation is a clearance-decision template rather than a numerically complete three-channel safety index.

Machines doi: 10.3390/machines14091073

Authors: Zeeshan Hamid Malik Emilio Sanjurjo Mario López-Lombardero Antonio J. Rodríguez Miguel Á. Naya Francisco González

Mechanical clearances are present in mechanical systems due to manufacturing tolerances, assembly imperfections, wear, and material deformation. Their presence can significantly influence dynamic behavior, vibration levels, and long-term component performance. As a result, the characterization, monitoring, and estimation of clearances have become important research topics. Considerable research has been devoted to the dynamic analysis of mechanical systems with clearances using multibody formulations, while more recent studies have explored hybrid model-based estimation techniques and data-driven methods for clearance identification. The development of such approaches requires experimental platforms capable of systematically reproducing and investigating clearance-induced dynamic behaviors. This work presents the development of a configurable slider–crank test bench designed for experimental investigation of mechanical clearance, which allows the selection of clearance location and magnitude, operating speed, and mechanism orientation. A multibody model was developed to support the system design, monitor its operation, and provide a numerical reference for the obtained experimental data, retrieved from accelerometers and encoders. The results obtained during experimental tests demonstrate that the platform generates repeatable measurements while remaining sensitive to variations in operating conditions and clearance configurations. Results also served to assess the ability of multibody formulations and clearance modeling approaches to correctly characterize the dynamic effects caused by joint clearances. Formulations that describe the system motion using minimal coordinates resulted in reduced uncertainty in simulation results. The selected contact models succeeded at capturing the effect of revolute joint clearances on the accelerations of the mechanism, establishing a first step in the detailed description of the contact dynamics that is necessary for the use of forward-dynamics simulation results in the development of hybrid model-based and data-driven clearance characterization methods.

Machines doi: 10.3390/machines14091072

Authors: Anjun Xu Qiongying Lv Bing Jia Lingyu Zhou Gan Qiu

To reduce high 1× vibration during staged speed-up of a Turbine Power Simulator (TPS) rotor, a staged incremental on-site balancing method based on a Genetic Algorithm–Salp Swarm Algorithm (GA–SSA) is proposed. SSA is a swarm-intelligence optimizer inspired by salps, gelatinous marine organisms that move collectively in chains. A one-dimensional Timoshenko-beam rotor model with lumped disks and equivalent bearing supports is established and validated using a three-dimensional ANSYS model. From meshes M3 to M4, the equivalent speed associated with the first lateral natural frequency changes by 0.23%. The first three critical-speed errors are 6.75–8.80%, while baseline 1× vibration-amplitude errors remain below 10% and phase errors below 7.1%. Speed-specific influence coefficients are then extracted to formulate a staged incremental balancing model based on the current measured vibration and cumulative correction state. In GA–SSA, the final GA population initializes SSA, and the historical GA best is used as the initial Food. Under equal function-evaluation budgets and 30 paired runs, GA–SSA shows search performance comparable to GA and improves the stability of standalone SSA. On-site tests at 10,358, 25,558, and 38,333 rpm reduce 1× vibration at both rotor ends by 79.0–86.4%, confirming the method’s engineering applicability.

Machines doi: 10.3390/machines14091070

Authors: Jingjing Wang Nellian Alagu Subramaniam Eddie Zhi En Tan John Hock Lye Pang

High-speed directed energy deposition (HS-DED) was employed to fabricate a high-quality tungsten carbide (WC)-reinforced Inconel 625 metal matrix composite (MMC) coating on 316L stainless steel. The exceptionally high scanning speed (~30,000 mm/min) significantly reduced thermal exposure during processing, promoting uniform WC particle retention, negligible porosity (<0.1%), and strong metallurgical bonding with the substrate. Microstructural characterization using SEM, EBSD, and XRD revealed a refined Inconel 625 matrix with limited WC dissolution and pronounced accumulation of geometrically necessary dislocations (GNDs), indicating strong heterogeneous deformation-induced strengthening. The resulting coating exhibited high hardness, superior shear bond strength (623 MPa), and a defect-free structure, outperforming conventional high-velocity oxy-fuel (HVOF)-sprayed coatings. These results demonstrate that HS-DED enables the fabrication of dense, well-bonded, and mechanically robust MMC coatings, offering a promising alternative to conventional thermal spray technologies for demanding wear and structural applications.

Machines doi: 10.3390/machines14091071

Authors: Junpeng Wang Chuang Liang Lu Li Jian Zhan Giuseppe Bianchi Sham Rane Fanghua Ye Ying Zhang

The adoption of advanced three-dimensional Computational Fluid Dynamics (CFD) tools for the research and design of Rolling-Piston Compressors (RPCs) is severely constrained by the absence of efficient and reliable grid generation methods. To address this issue, this paper proposes a novel analytical grid generation method for the rotor fluid domain of RPCs based on the User-Defined Nodal Displacement (UDND). This method splits the rotor fluid domain into a vane region, a transition region and a core region according to geometric characteristics. The number of circumferential nodes in each region is adaptively determined based on the mapped lengths of the corresponding inner and outer boundaries, while node number normalization is employed to ensure precise control of the total number of nodes. Numerical tests demonstrate that the proposed method can generate O-type structured meshes with consistent topology and adaptive node allocation over the entire range of rotor rotation angles. The proposed method was verified by reference indicated pressure measurements on a small-scale RPC for refrigeration and air-conditioning applications, yielding mean absolute percentage errors of 6.30% and 7.42% and maximum pointwise relative errors of 12.70% and 20.99% at 80 and 120 Hz, respectively. The proposed method reduces the preprocessing time required for a typical CFD model of the machine from approximately 48 h to only 54 s. The improved quality and robustness of the generated mesh enhance the stability and convergence behavior of the solver, thereby enabling the use of advanced physical models, such as the real-gas equation of state, in the design and analysis of RPCs. This paper presents a rapid and reliable meshing strategy for CFD simulations of RPCs.

Machines doi: 10.3390/machines14091069

Authors: Bilal Sevim Mumin Tolga Emirler

This study presents an innovative, integrated control architecture combining adaptive online linear quadratic integral (AOLQI) and Stanley geometric control systems for autonomous vehicle path tracking. A key feature of the proposed framework is the offline optimization of the AOLQI parameters using the Particle Swarm Optimization (PSO) algorithm. To address varying road conditions, a Forgetting Factor Recursive Least Squares (FFRLS) algorithm is employed for real-time tire cornering stiffness estimation, complemented by a Kalman–Bucy filter for high-fidelity vehicle side-slip angle observation. The efficacy of this architecture is validated through MATLAB/Simulink and IPG CarMaker co-simulations across demanding benchmarks, including a 100-m radius circular path, the high-speed Hockenheim race track and the high-curvature Stelvio Pass road profile. Numerical evaluations demonstrate that, compared to the conventional LQI, the proposed approach achieves reductions in root mean square error (RMSE) of 44.85%, 24.76% and 51%, and decreases in integral square error (ISE) of 69.2%, 43.16% and 75.86% respectively, in these different scenarios. These results confirm that using an integrated approach with adaptive online LQI and Stanley control, alongside an estimation layer, ensures superior tracking precision and performance improvements across extreme road geometries.

Machines doi: 10.3390/machines14091068

Authors: Lin Chen Qinjie Hu Tianyang Wang Jiawei Qin Kanlun Tan Li Yang Qi Li Dafang Wang

Due to the higher power density of silicon carbide (SiC) inverters in electric vehicles (EVs), effectively managing conducted electromagnetic interference (EMI) has become a vital aspect of inverter design. The complex power topology of SiC inverters increases the complexity of different types, phenomena, and mechanisms of conducted EMI, making the selection of appropriate suppression methods more challenging. Many studies have examined the mechanisms of conducted EMI and their suppression techniques. However, the fast switching transients of SiC devices can affect an automotive traction inverter at multiple physical levels, ranging from the gate-drive circuit and isolation interface to the external power terminals. These phenomena are closely related through their common switching excitation and parasitic coupling networks, but they should not all be interpreted as equivalent conducted-emission phenomena. To provide a structured engineering perspective, this review organizes the relevant disturbances using a source–path–victim framework and examines three representative paths: gate-loop crosstalk, common-mode (CM) coupling across the isolated gate-drive interface, and system-level CM/DM-conducted emissions. The corresponding mitigation techniques and their applicability to EV traction inverters are subsequently reviewed.

Machines doi: 10.3390/machines14091067

Authors: Noé Labeyrie Georges Salameh David Chalet Michael Deligant

To reduce greenhouse gas emissions, fuel cell powertrains represent a promising alternative for heavy-duty transport. Such demanding applications require an extended operational lifespan, which calls for models able to accurately map internal states as a function of system architecture and control strategy. This work presents a pseudo-2D macro-homogeneous proton exchange membrane fuel cell model, discretized along the flow channels, in which the various transport phenomena are resolved between layers but not within their thickness. This choice reflects the model’s purpose: integration into complete system models to support system architecture studies, which requires a suitable trade-off between computation time and representativeness of system-imposed operating conditions. Kulikovsky’s analytical approximation is used to compute the voltage losses in the catalyst layer, preserving an accuracy close to a model with a fully discretized catalyst layer thickness. The model is integrated into a system featuring anode recirculation and no cathode humidification to study the sensitivity of humidity distribution to operating parameters. Simulations show that the anode recirculation rate and the temperature difference between inlet and outlet are the two main operating conditions governing spatial humidity distribution, while coolant inlet temperature, pressure, and cathode stoichiometry predominantly affect the absolute humidity within the stack.

Machines doi: 10.3390/machines14091066

Authors: F. Reichel G. N. Sahu A. Otto S. Ihlenfeldt

This paper presents a virtual sensor for the in-process prediction of cutting forces from feed drive current measurements in milling processes via inverse filtering. Components of the feed drive current in ball-screw drives that are related to inertia, friction, and gravity are separated from the cutting-force-related component via models or air-cutting experiments. Impact hammer tests are then used to identify the transfer function between forces at the tool tip and the corresponding response at the feed drive. The proposed inverse filtering approach completes the virtual sensor for online monitoring of cutting forces based on feed drive current signals. Compared to existing approaches, which are mainly based on Kalman-filter or deep learning models, this method avoids the additional effort for modeling, parameter identification and generation of training data. Experimental results are presented for cutting tests on a three-axis turn-milling center. The prediction error between the virtual sensor and the measured cutting forces lies between 6% and 17%, depending on the cutting parameters. In general, the virtual sensor can be implemented in any feed drive system with a minimal effort for parameter identification.

Machines doi: 10.3390/machines14091065

Authors: John Alexander Baca Rodriguez Christian Estanislao Barrientos Quispe Mahdi Tavakoli Deyby Huamanchahua

The human ankle exhibits complex multiplanar behavior involving plantarflexion, dorsiflexion, inversion, eversion, and coupled orientation changes that are essential for balance and terrain adaptation. This work presents the design, kinematic evaluation, mechanical implementation, and control validation of a transtibial prosthesis based on a parallel robotic mechanism. Three candidate architectures, 3-SPS-1-S, 3-UPU-1-S, and 3-UCU-1-S, were compared through inverse and forward kinematics, orientational workspace, Jacobian conditioning, constructability, and mechatronic-integration criteria. The corresponding workspace coverages were 74.1%, 75.7%, and 72.2%, respectively. The 3-UPU-1-S architecture exhibited a median Jacobian condition number of 12.63, a 95th-percentile value of 20.56, no numerically singular configurations within the evaluated feasible workspace, and the highest VDI-2225 technical score (0.913), supporting its final selection. The selected mechanism was manufactured and integrated into a functional laboratory prototype with distributed ESP32-S3-based electronics, position sensing, inertial measurement, and closed-loop actuation. Experimental periodic tests showed that the decentralized PID controller achieved dominant-axis RMSE values of 3.53° in pitch during dorsiflexion–plantarflexion and 4.61° in roll during inversion–eversion. A revised formal LQRI controller was evaluated separately using the identified actuator-space model. Its nominal closed-loop system was asymptotically stable, with maxRe(λ)=−1.6896, and robustness simulations showed mean-RMSE reductions of approximately 9.4–16.6% relative to PID across the evaluated perturbation scenarios. These results demonstrate the feasibility of the proposed 3-UPU-1-S mechanism as an integrated laboratory platform for multiplanar transtibial-prosthesis research while identifying the need for future dynamic load-bearing and user-centered validation.

Machines doi: 10.3390/machines14091064

Authors: Jiayu Fu Jinfa Guan Junqing Chen

Unequal load sharing between collector strips causes local contact deterioration in metro rigid overhead contact line (ROCL) systems, whereas conventional equivalent-strip models cannot resolve independent strip contact or friction-induced panhead pitching. This study develops a coupled pantograph–ROCL model with independent vertical degrees of freedom for the two strips and a panhead pitch degree of freedom. The ROCL is represented by planar Euler–Bernoulli beam elements with Hermite interpolation and coupled to the pantograph through two moving unilateral contacts. The baseline model is validated against measured contact-force statistics from a Guangzhou Metro line. A representative low-temperature, low-humidity, snow-free scenario is then introduced through test-informed friction variation, modified support stiffness and small support-height deviations. The scenario has little effect on mean contact force but increases force fluctuation, impact peaks, panhead pitching and local poor-contact risk. Sensitivity-guided derivative-free optimisation of local panhead parameters reduces the maximum pitch angle, total-contact-force standard deviation and dual-strip load-imbalance index by 38.24%, 23.09% and 44.43%, respectively. The model provides a mechanical framework for evaluating friction-induced pitch vibration and improving dual-strip load sharing in rigid current-collection systems.

Machines doi: 10.3390/machines14091063

Authors: Tanya Titova Rosen Kosturkov

Compressed-air leaks create persistent parasitic demand, but machine-level condition monitoring is difficult because air consumption changes strongly with operating mode. LEADI (Leak Energy Anomaly Detection Index) was developed as an operating-mode-aware procedure that evaluates the deviation of directly measured flow rate from a local reference baseline derived from a stable post-repair condition with maintained pressure and low within-window variability. The method was developed on days 1–5 and evaluated on held-out days 6–7 from two one-week campaigns conducted before and after implementation of the prescribed corrective actions. With 60 min windows, LEADI flagged 19/19 evaluable pre-repair and 0/17 post-repair windows, with diagnostic coverage of 39.6% and 35.4%, respectively. A simple fifth-percentile flow comparator without operating-mode selection flagged 47/48 versus 1/48 windows. This shows that the low-flow region itself contains strong discriminatory information for separating the two periods. The role of the operating-mode layer is to restrict engineering interpretation to pre-specified eligible operating conditions. The flow-rate difference within the diagnostic operating condition was 122.0 L/min (95% CI 114.6–132.3). Over a common 168 h basis, measured volume decreased by 1370.8 m3 (28.90%), while a separate check normalized by pressurized time gave 28.12%. Because the specific energy consumption of the compressor station was not measured, the energy effect is reported only as a scenario for the same 168 h. The results support the applicability of LEADI as a selective decision-support layer for the investigated asset and the two observed conditions, without establishing universal leak detection or causal attribution of the observed change to individual defects.

Machines doi: 10.3390/machines14091062

Authors: Peng Xu Yiding Liu Huaming Zhang Yousheng Yang Dian Liu Yonggang Xu Lei Feng

Wind turbine bearings operate long-term under complex and variable operating conditions, where fault impulse characteristics are easily submerged by strong noise. Traditional graph signal processing-based bearing fault diagnosis methods are limited by fixed graph topology, empirical feature selection and poor noise robustness. This paper proposes an adaptive frequency graph spectrum (AFGS) model for bearing fault diagnosis. The model constructs graph signals in the frequency domain and determines the core analysis interval adaptively via eigenvalue sequences, which eliminates fixed topology constraints. Combined with a fast bisection search framework and a correlation spectral negative entropy (CSNE) index sensitive to periodic fault impulses, the proposed method realizes fully automatic optimal band selection without manual intervention and improves noise resistance. The AFGS method first transforms vibration signals via fast Fourier transform and constructs frequency-domain graph features based on Laplacian matrix decomposition. The optimal fault characteristic band is adaptively determined using the bisection framework and CSNE criterion. Finally, signal reconstruction and envelope spectrum analysis are implemented for fault identification. Simulation results under −3 dB low signal-to-noise ratio show that AFGS can effectively extract the 1st to 8th fault harmonics. Further validation on the measured inner and outer race fault signals of 6205 bearings demonstrates that the proposed method can clearly identify fault characteristic frequencies and their multi-order harmonics. Comparative tests with Fast Kurtogram and Autogram indicate that the two benchmark algorithms only extract limited low-order harmonics under simulated noise and completely fail in practical strong noise environments. Experimental results verify that AFGS outperforms conventional methods in band localization accuracy, noise suppression, and fault feature extraction completeness, providing a reliable solution for the bearing fault diagnosis of rotating machinery under complex working conditions.

Machines doi: 10.3390/machines14091061

Authors: Xiaole Guan Xin Jin Zhijing Zhang Zhilong Luo Chan Wang

Casing assembly deviations can disrupt aero-engine support alignment and induce abnormal vibration. However, the mechanism by which bearing-seat coaxiality errors generate superharmonic responses remains insufficiently understood. This study investigates how such deviations propagate through support misalignment and a spline coupling to influence the vibration response of a coupled rotor–casing system. A geometric relationship is formulated to transform the misalignment of the support into equivalent parallel and angular initial offsets at the spline coupling. An additional excitation model for the spline coupling, accounting for meshing stiffness, transmitted torque, tooth-side clearance, unilateral tooth contact, and relative whirl motion, is incorporated into a reduced-order whole-engine dynamic model. The integrated model is evaluated under a range of experimentally measured coaxiality conditions. Numerical simulations predict critical response regions near 16,000 and 23,000 r/min, along with subcritical resonance peaks at approximately 7800 and 12,500 r/min that are attributed to superharmonic excitation mechanisms rather than conventional mass unbalance alone. Experimental results indicate that the 0.234 mm coaxiality condition yields larger vibration amplitudes, additional low-speed resonance peaks, and more pronounced 2X–4X harmonic components compared with the 0.069 mm condition, thereby corroborating the proposed assembly deviation mechanism under cold-state structural dynamic conditions.

Machines doi: 10.3390/machines14091060

Authors: Yubo Shao Huibo Chang Lingyun Yang Wei Li

Feature distributions shift under variable-speed gearbox operation, which can cause a model trained only on healthy samples to misclassify normal operating changes as anomalies. A semi-supervised anomaly detection method is proposed in this study. Using healthy data, the method first fits speed-dependent trends for the selected time- and frequency-domain statistics, and the deviations from these trends form the statistical residuals. Computed order tracking then converts the vibration signal to the angular domain, where five mechanism features describe meshing energy, harmonic structure, sideband modulation, and order-spectrum entropy. Removing the corresponding healthy speed trends yields the mechanism residuals. Robust Bounded Health-Consistency Weighting (RB-HCW) weights these residuals according to their variability in healthy data before they are fused with the statistical residuals and modeled by Deep Support Vector Data Description (DeepSVDD). The Sequential Bayesian Queue-Based Alarm (SBQA) module then confirms whether abnormal decisions persist across successive windows. Across the four fault types under the two separately modeled load conditions, the proposed method achieved macro-averaged true positive rate (TPR), accuracy (ACC), and F1-score values of 93.31%, 92.58%, and 94.02%, respectively.

Machines doi: 10.3390/machines14091059

Authors: Aydın Hacı Dönmez Yaşar Mutlu Pegah Mutlu

This study presents a predictive modeling framework for estimating transient flow rates in PWM-driven solenoid valves. Building on a previously validated dynamic model, the proposed framework enables flow prediction under varying process conditions and valve configurations. Flow rates at fully open conditions are obtained using computational fluid dynamics (CFD) and validated experimentally. CFD analyses are further performed at partial valve openings, and the resulting data are incorporated into a numerical algorithm based on piecewise linear interpolation for prediction throughout the opening–closing cycle of the valve. The model is validated under PWM operation, and parametric analyses are conducted to examine the effects of duty ratio, period, and coil voltage. The maximum difference between the experimental and numerical results was 1.3% under fully open conditions and 2.8% under PWM operation, demonstrating good agreement across the investigated conditions. By incorporating flow characteristics at intermediate spool positions that cannot be directly measured experimentally, the proposed approach allows accurate prediction of both transient and time-averaged flow rates. This provides a computationally efficient alternative to fully coupled transient CFD simulations.

Machines doi: 10.3390/machines14091058

Authors: Paraskevi Zacharia Styliani Kontaki Konstantinos Moustris Constantinos Stergiou

Industrial fault detection and diagnosis are essential for maintaining operational reliability and minimizing performance degradation in process industries. This study presents an integrated machine learning framework for fault detection, fault-type classification, and fault severity estimation using a synthetic chemical-process time-series dataset comprising six reactors operating under multiple conditions. The framework combines a five-class fault diagnosis model with a gated severity estimation stage that is activated only when a fault is detected, enabling simultaneous assessment of process condition and operational impact. Eight process variables were selected through statistical and process-oriented analysis, while one-minute difference features and reactor identity information were incorporated to capture short-term process dynamics and equipment-specific operating characteristics. The framework was evaluated using an episode-aware methodology incorporating fault-episode partitioning, leakage-prevention measures, grouped cross-validation, and episode-level analysis. The selected classification model achieved a balanced accuracy of 76.25% and a macro F1-score of 82.44%. For severity estimation, the complete end-to-end pipeline achieved R2 = 0.153 across active-fault observations, illustrating the impact of fault detection errors on downstream severity assessment. When evaluated across all observations, including predominantly normal conditions, the corresponding R2 increased to 0.871. Under an oracle scenario using the true fault type, severity estimation achieved R2 = 0.920. The results provide fault-specific and episode-level insights and support a proof of concept within this synthetic industrial process environment.

Machines doi: 10.3390/machines14091057

Authors: Pierre Boulanger

A flexible part has no unique shape until it is constrained, which makes dimensional inspection difficult. Standard practice clamps it in a dedicated jig and probes it with a coordinate measuring machine or a range sensor. We replace the jig with virtual fixturing. From partial range views of the unfixtured part, the pipeline recovers a coarse pose between the scan and the CAD model using a robust geodesic bilateral curvature algorithm, deforms the model towards the scan by non-rigid registration, and computes deviations along the model surface normal to decide whether the part is in tolerance. On a prismatic part and a game-controller housing, the method is more accurate than optimal-step non-rigid ICP, radial basis function FEM, and coherent point drift, because the generated deformations come from an operator closely related to the proposed method’s own regularizer, those margins favor it by construction and are reported with that caveat. On a generated thin-shell test part the measurement uncertainty of the implementation is measured at about 0.039 mm, dominated by the registration rather than by the sensor. The pipeline is then exercised on a physically scanned injection-molded engine cover, a 612 mm part whose free-state residual against its nominal model is 4.62 mm at the verified global optimum. No independent coordinate-measuring-machine reference was available for that part, so this experiment is reported as a free-state residual and a controlled comparison with and without the feature set, not as a statement of absolute measurement accuracy.

Machines doi: 10.3390/machines14091056

Authors: Reza Naseri Brennan L. Gedney Dimitrios C. Rizos

Train derailments pose a critical failure mode in railway systems, often resulting in severe safety hazards and significant financial losses. Understanding how train loading patterns interact with track deficiencies is essential for effective failure analysis and prevention. This paper introduces the Rapid Vehicle–Track Interaction (R-VTI) as a framework to simulate the complexities of dynamic train–track interactions. Central to the framework is the novel Pseudo-Dynamic Coupling (PDC) technique, which enables computation of wheel–rail dynamic forces with substantially greater computational efficiency than currently used coupling techniques. The R-VTI framework supports a wide range of solver techniques and subsystem coupling schemes, making it adaptable for different simulation requirements. The framework is validated against Federal Railroad Administration field measurements, achieving agreement within 5% error. A case study of different train–track configurations shows that the framework can quickly detect when loading patterns and track conditions exceed derailment thresholds. Axle-level results reveal that unloaded cars near the front or middle of the train increase the likelihood of derailment-failure modes. The efficiency of the R-VTI framework enables large-scale scenario analysis, supporting both optimized loading strategies and targeted track maintenance. By providing a robust and scalable solution, the R-VTI framework advances derailment potential assessment practices, offering a practical tool for improving railway safety and operational resilience.

Machines doi: 10.3390/machines14091055

Authors: Zhonghao Liu Gang Yu Tian Ran Lin

In this paper, we propose an instantaneous chirp-rate-driven adaptive window Chirplet transform algorithm for the analysis of strong time-varying nonlinear frequency-modulated signals with uncorrelated components. In this approach, the length of the sliding window in the Chirplet transform is dynamically adjusted according to the estimated instantaneous chirp rate of each signal component of an initial time–frequency result from short-time Fourier transform (STFT). A boundary constraint determined from the modal support intervals of the signal is utilized to restrain the allowable searching frequency range of the instantaneous frequency (IF) trajectories and incorporated into a cost-function-based IF extraction method to improve accuracy in the IF estimation. The effectiveness of the proposed algorithm is validated using a simulated nonlinear frequency-modulated (FM) signal with two uncorrelated components, and two sets of experimental bearing vibration signals. It is shown that the proposed algorithm can accurately track the frequency modulation of a strong FM signal dynamically to render an accurate estimation of the IFs and modal amplitudes of a strong FM signal. A comparison study also verifies that the proposed algorithm can produce a better energy-concentrated time–frequency result compared to other commonly employed time–frequency analysis techniques, particularly when the signal is contaminated by noise.

Machines doi: 10.3390/machines14091054

Authors: Adilanmu Sitahong Ruili Zhao Yiping Yuan Xinpeng Nie Peiyin Mo

Dynamic job shops must absorb new orders, machine failures and processing time variations while operating under increasingly demanding energy and carbon constraints. In such settings, an offline schedule may become obsolete soon after release, especially when production and energy states evolve on different time scales. Digital twins, data-driven models and artificial intelligence methods now make it possible to sense shop floor changes, anticipate their effects and revise schedules through feedback. This review organises the emerging literature through a ‘four loops and one layer’ framework: perception, modelling and prediction, intelligent decision-making, and execution feedback form the operating cycle, while continuous learning spans successive scheduling rounds. Studies are examined along three distinct but related dimensions—dynamic events, green objectives and digital intelligence methods. Within this D-G-I framework, the literature reveals a move from static optimisation to adaptive scheduling, from efficiency-centred formulations to coordinated efficiency–energy–carbon objectives, and from stand-alone rules towards combinations of data, models and domain knowledge. Yet the evidence remains uneven. Data–model coupling is often weak, transfer across production settings is limited, and genuinely closed-loop industrial validation is rare. These limitations make explainable decision-making, cross-scenario adaptation and digital twin-enabled closed-loop optimisation central priorities for subsequent research.

Machines doi: 10.3390/machines14091053

Authors: Shao-Xuan Zhang Hai-Dong Song Yi-Yao Zhang

Steel rails are critical components of industrial dynamic transportation systems, and their in-service fault diagnosis demands reliable discrimination of multiple defect categories under multi-mode Lamb-wave dispersion and sample-level physical-parameter drift. The rail surface is modelled by a thin metal plate instrumented with two EMAT probes (the standard laboratory surrogate for in-service rail inspection), and the proposed architecture is evaluated on this rail-equivalent plate geometry. Purely data-driven one-dimensional classifiers plateau near 80% test accuracy on a 25,000-sample simulated EMAT A-scan benchmark, while conventional physics-informed neural networks (PINNs) that inject the physical prior only at the loss-function level fail to break this ceiling. We propose a hybrid data–physics residual network, the proposed EMAT-PINN, that couples a convolutional backbone with a logit-orthogonal four-head architecture tying each defect class—hole, crack, corrosion, weld—to one simulator-derived physical quantity (reflected energy, S0/A0 ratio, arrival time, or dispersion shift) via a bias-free additive projection of the class logit. The bias-free construction guarantees that deleting or zeroing any head collapses the affected class logit to the shared baseline, so the remaining heads cannot reroute around the missing head—a structural non-replaceability that supports explainable fault diagnosis. Combined with a four-term physics regression loss (λphys=2.0), the resulting proposed EMAT-PINN attains 95.05% test accuracy at only 0.195 M parameters, with every knockout ablation dropping the model below the 80% threshold commonly referenced as a practical acceptance benchmark. This per-class mapping provides an auditable link between the model’s internal representation and the physical scattering mechanism behind each decision, directly supporting explainable fault diagnosis in industrial deployment.

Machines doi: 10.3390/machines14091052

Authors: Vasilija Sarac Dragan Minovski Sara Aneva Darko Bogatinov

Line-start synchronous motors (LSSMs) have emerged as a promising alternative to induction motors in response to increasingly stringent energy-efficiency requirements. However, the salient-pole permanent-magnet rotor topology has been comparatively less studied than other LSSM rotor configurations, particularly with respect to the influence of its design parameters on motor efficiency and dynamic performance. Initially, the effects of the number of rotor bars and the dimensions of the squirrel-cage winding on motor performance are investigated, and the optimal configuration with respect to starting torque and synchronization capability is selected. The chosen design is then subjected to efficiency optimization by varying four design parameters. An optimetric analysis is employed to evaluate numerous parameter combinations under predefined operating conditions, generating a wide range of motor models. The configuration achieving the highest efficiency is subsequently identified and selected. In addition, the optimization with same design parameters is carried out using Genetic Algorithms (GA), confirming the results obtained through the optimetric analysis. The transient responses of speed, torque, and current are subsequently investigated for both the initial and optimized motor model. The analysis provides insight into the effects of the optimization process on motor starting performance, synchronization capability and steady-state operation enabling appropriate conclusions to be drawn.

Machines doi: 10.3390/machines14091051

Authors: Dongmei Wang Henghui Liao Ye Chu Guo Tang Hao Chang

With the growing global emphasis on environmental protection, energy conservation, and emission reduction, along with rapid advances in precision machinery and manufacturing technologies, energy-efficient multi-blade centrifugal fans have become a key research area worldwide. These devices, commonly referred to as multi-blade centrifugal fans, utilize an impeller with multiple blades to transport air or gas, operating on the same fundamental principles as conventional centrifugal fans. Due to their ability to deliver high flow rates with high efficiency and stable performance, they are widely used in ventilation, air conditioning, and industrial applications. This paper provides a comprehensive review of recent advances in the research and development of multi-blade centrifugal fans. It covers key aspects of component design and optimization, including the impeller, volute, collector, and rim clearance. The review also addresses critical issues, including rotating stall, vibration, noise generation, and material selection. Furthermore, it highlights emerging perspectives, including the application of entropy production theory to elucidate flow mechanisms, strategies for performance optimization, advanced technologies for material innovation, and protective devices to enhance operational reliability and functionality. This review provides guidance for future research and development aimed at improving the efficiency and performance of multi-blade centrifugal fans across diverse applications.

Machines doi: 10.3390/machines14091050

Authors: Mingzhen Zhang Anwen Shen

Nonsmooth gear-contact vibration under variable-speed operation is strongly affected by mesh-phase evolution, backlash-induced contact switching, and unilateral tooth contact. This study investigates local line-of-action vibration suppression in a single-stage spur-gear pair performing a repetitive acceleration–braking–cruising task. The modelling contribution is a control-oriented hybrid-coordinate formulation that separates the prescribed mean-speed motion from local dynamic transmission error (DTE), reconstructs the time-varying mesh stiffness (TVMS) phase from the mean angle and local relative displacement, and retains a distinct mean-coordinate phase for transmission error (TE). The learning contribution is a bounded line-of-action force strategy that combines finite-time application, low-pass filtering, plateau DC removal, amplitude and rate constraints, and trial-level contact-response acceptance and rollback. Thus, an input-feasible candidate can still be rejected when its executed contact response violates the prescribed limits. Under the nominal same-initial-condition baseline, the trade-off iteration reduces DTE and mesh-force AC RMS by 11.52% and 24.85%, respectively. Contact loss decreases from 3.35% to 0.55%, and re-engagements decrease from 29 to 5. In 100 paired perturbation samples, 94 cases improve both RMS measures. The results support the proposed framework within the tested numerical task and perturbation range.

Machines doi: 10.3390/machines14091049

Authors: Ali Khalfallah Carlos Leitão Elango Natarajan

Manufacturing systems face increasing pressure to reduce energy use, material consumption, waste generation, and environmental emissions while maintaining product quality, productivity, and economic viability [...]

Machines doi: 10.3390/machines14091048

Authors: Yiqiao Li Yang Xiao Jing Huang Yali Jiang Luyuan Gong Yali Guo Shengqiang Shen

Compared to conventional fuels, hydrogen fuel offers advantages such as high specific heat capacity, low boiling point, and zero carbon emissions, demonstrating significant potential for green energy conservation and sustainable development in the aviation field. This paper reviewed the latest advances, technical challenges, research hotspots, and future development directions related to the response and regulation of various components within the hydrogen transportation and thermal management systems for aeroengines, filling a gap in the existing literature. (1) As to the fuel of aeroengines, the heat exchanger efficiency of the heat exchanger employed for intercooling while utilizing hydrogen fuel can reach 10.63 times that of kerosene, and the turbine inlet temperature is significantly reduced under sea-level takeoff conditions. Under high-altitude supersonic flight conditions, its specific fuel consumption is approximately 0.33–0.40 times that of kerosene. However, aeroengines also confront challenges such as the requirement for high-efficiency thermal insulation and the control of cold energy losses. (2) When the pressure regulation accuracy of hydrogen storage containers, hydrogen supply stability, and thermal management coordination are ensured, the fuel weight index can be optimized to 0.62 during hydrogen transportation, significantly reducing the impact of the hydrogen storage system on the payload capacity of aircraft models. Nevertheless, crucial components involved in hydrogen transportation, such as cryogenic liquid hydrogen tanks, are vulnerable to significant temperature fluctuations, which can cause pressure oscillations, response delays, and seal failures, thereby affecting the stability of the hydrogen fuel supply. (3) In the thermal management system of hydrogen-fueled aeroengines, the fuel consumption and transportation cost of the engine compared with the unoptimized baseline system are reduced by 14.54% and 11.74% through regulating important component parameters such as heat exchanger power. However, the thermal management system confronts challenges during the heat exchange among hydrogen fuel, high-temperature airflow, and residual heat, including strong coupling among multiple components and insufficient real-time sensing capability for dynamic thermal loads. Future development should shift from “passive adaptation” to “active regulation and control,” aiming to achieve dynamic decoupling of temperature, pressure, and stress fields under strongly coupled multi-heat source operating conditions, along with coordinated regulation and matching of multi-component dynamic responses.

Machines doi: 10.3390/machines14091047

Authors: Yingjie Li Libao Yang

Shipborne photoelectric telescopes must consider the influence of the ship on structural stability during offshore operations. In this study, a 1.2 m shipborne photoelectric telescope tracking frame was investigated, and the inertial acceleration induced by hull motion was analyzed. Three ship motion modes were simplified as independent harmonic motions, and their motion equations and parameters were derived. The structural deformation under combined ship motions was evaluated by applying inertial loads through finite element analysis. The maximum deformations along the altitude axis parallel and perpendicular to the bow-stern line were both less than 0.09 mm, indicating negligible structural impact. A ship motion simulation system was developed and validated using an LMS modal analyzer. Modal analysis was performed to obtain the first six mode shapes, and sensor locations were optimized accordingly. The measured frequencies of the first six modes were 43.10, 47.47, 62.76, 74.52, 126.84, and 146.32 Hz, respectively, with the fundamental frequency exceeding the simulated value of 34.32 Hz.

Machines doi: 10.3390/machines14091046

Authors: Sibusiso Gule Elsie Fezeka Swana Lutendo Muremi

Induction motors play a vital role in industrial operations; however, stator inter-turn short-circuits faults remain a common and critical source of failure. This paper presents a machine learning-based diagnostic approach for detecting stator inter-turn short-circuit faults in three-phase induction motors operating at 50 Hz. Secondary data from a controlled test bench were processed using Power Spectral Density to determine energy distribution and guide the design of a Butterworth bandpass filter (20–350 Hz). The filtered signals were then analyzed using the Hilbert Transform to extract statistical features, which were ranked using the Minimum Redundancy Maximum Relevance algorithm to identify the most discriminative parameters. Two supervised classifiers, Support Vector Machine and Random Forest, were developed and validated using MATLAB’s Classification learner app with 5-fold cross-validation. The Support Vector Machine achieved an accuracy of 94.19%, while the Random Forest model achieved 99.51% with macro and F1-scores of 0.9951 and near-perfect area under the curve values. The results confirm that the Random Forest classifier provides superior generalization, sensitivity, and robustness in fault detection compared to Support Vector Machine. This study successfully demonstrates that combining Power Spectral Density, Hilbert Transform, Minimum Redundancy Maximum Relevance, and ensemble learning yields a highly effective framework for predictive maintenance and reliable fault diagnosis in industrial motor applications.

Machines doi: 10.3390/machines14091045

Authors: Mohamed El-Sayed M. Sakr Mohamed A. Moustafa Hassan Tamer Kamel

The rapid advancement of power electronic devices has accelerated the widespread adoption of Line-Commutated Converter (LCC) and Voltage Source Converter (VSC) technologies as leading solutions for high-voltage industrial applications. These technologies have become fundamental components of modern high-voltage direct current (HVDC) transmission systems and advanced Industrial Machine-Drive (IMD) systems. This review presents a comprehensive comparison between conventional LCC technology and the more recent VSC technology, highlighting the operational advantages, limitations, and application suitability of each approach. In addition, it provides an in-depth examination of hierarchical control architectures employed in both LCC-HVDC and VSC-HVDC systems. As these systems are increasingly required to operate closer to their performance limits, the implementation of robust and efficient control strategies has become essential for ensuring stability, reliability, and optimal performance. Consequently, a wide range of control techniques has been developed to address the inherent nonlinearities and parameter uncertainties present in power systems. This review evaluates and compares both conventional and advanced control methods, including Proportional–Integral (PI) control, Variable Coefficient PI (V-PI), Fuzzy PI, Self-Tuning Fuzzy PI (STF-PI), Fractional Order PI (FOPI), Variable Coefficient Fractional Order PI (V-FOPI), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Model Predictive Control (MPC). Their performance is assessed across a range of operating conditions, with emphasis on dynamic response, robustness, and overall control effectiveness.

Machines doi: 10.3390/machines14091044

Authors: Yuhang Zeng Ping Lou Tianren Ming Ruochen Gao Jianmin Hu

Industrial equipment-maintenance knowledge is scattered across technical manuals, historical work orders, and field experience. Rapidly retrieving these heterogeneous sources and generating accurate guidance are essential to reducing downtime losses and ensuring operational safety. General-purpose large language models perform well in open-domain question answering, but their pretraining corpora lack industrial maintenance terminology and diagnostic reasoning patterns, causing severe mistakes in direct applications. Semantic-similarity-based retrieval-augmented generation can introduce domain knowledge to reduce hallucinations, yet it cannot preserve causal direction and procedural-order constraints. This limitation can logically misalign retrieved evidence and misguide maintenance decisions. Field queries also vary in wording and information completeness and often contain equipment abbreviations, colloquial transcriptions, and omitted key details, making fixed retrieval and response strategies unreliable. Our framework combines progressive fine-tuning with dual-path evidence fusion. A perplexity-driven sample-difficulty partition organizes two-stage fine-tuning so that the model learns basic domain knowledge before complex diagnostic reasoning, mitigating domain hallucinations. Parallel knowledge-graph and vector retrieval uses graph topology to preserve recorded relation directions and source procedures to supply operating-sequence information. A three-level router driven by retrieval necessity and entity-matching status selects direct answering, vector retrieval, or joint graph–vector retrieval for queries with different levels of information completeness. Independent tests, noisy-query experiments, and blinded expert evaluation show improved answer accuracy, logical consistency, and noise robustness. Relative to the base model, two-stage fine-tuning improves BLEU and ROUGE by approximately 14% and 7%, respectively. The results indicate that progressive domain learning and structured external evidence can provide reliable decision support for industrial equipment maintenance.

Machines doi: 10.3390/machines14091043

Authors: Hanzhengnan Yu Zhipeng Wang Jingyuan Li Fengbin Wang Yongkai Liang Hao Zhang Yu Liu

Auxiliary energy consumption rises sharply at low temperatures, reducing the accuracy of driving range prediction and vehicle energy management. Most previous studies have focused on battery electric vehicles, whose operating characteristics do not fully represent the powertrain architecture of range-extended electric vehicles (REEVs). This study analyzes REEV auxiliary energy consumption and develops prediction models for operation at low temperatures. Approximately 3600 km of actual road driving data were collected. Auxiliary energy consumption was examined across four dimensions: power mode, trip scale, thermal management load, and range-extender operating share. Engine waste heat reduced auxiliary energy consumption by more than 59% in range-extended mode compared with pure-electric mode. Random Forest (RF), Least-Squares Boosting (LSBoost), and Multilayer Perceptron (MLP) methods were used to develop a trip-scale auxiliary energy consumption prediction model (trip-scale model) and a second-scale auxiliary energy consumption prediction model (second-scale model). The best test-set R2 was 0.826 for the trip-scale model. For the second-scale model, R2 increased from 0.857 at 30 s to a maximum of 0.872 at 60 s; considering that doubling the sample duration yielded an R2 improvement of only 0.015, the 30 s LSBoost model was selected for subsequent integrated prediction. In the integrated application, the selected model predicted the mean auxiliary power over the remaining trip to estimate the remaining auxiliary energy. Although the departure estimate had a 9.55% error, iterative updates kept the entire estimate close to the measured value, with a maximum absolute residual of 0.022 kWh.

Machines doi: 10.3390/machines14091042

Authors: Chenying Hu Yu Ning Jintao Gu

The adoption of carbon fiber-reinforced polymer (CFRP) composites in aircraft structures has significantly reduced structural weight but compromised mid-frequency sound insulation performance. To address this issue, this study develops a lightweight membrane-type acoustic metamaterial design targeting the 2000 Hz sound insulation valley of aeronautical composite panels. An impedance tube test platform was constructed to characterize the full-frequency sound transmission loss (STL) of CFRP specimens, and a structure–acoustic coupled finite element model incorporating equivalent boundary stiffness was established and validated. The mean absolute error (MAE) of the simulation above 1000 Hz is within 3 dB, with a maximum single-point error of 3.9 dB, satisfying the engineering accuracy requirement for most frequency points. Under the constraint of no more than 5% weight increase, a forward-design methodology for membrane metamaterials is proposed based on modal analysis and local resonance tuning. Experimental results show that the proposed design achieves a 16.5 dB STL enhancement at 2000 Hz with a 2.29% weight increase under normal incidence conditions at the unit-cell level, exceeding the 3 dB technical requirement. This work provides a practical engineering reference for lightweight mid-frequency noise control in aircraft cabin applications.

Machines doi: 10.3390/machines14091041

Authors: Wenyu Zhao Zhenguo Gao Yuhui Yang Yuanxiang Guo Zhijue Huang Peng Zhao Xueshan Gao

This paper presents a delay-aware observer-side voltage-source inverter (VSI) nonlinearity compensation chain for medium- and high-speed sensorless control of permanent magnet synchronous motors (PMSMs). The method reduces the observer-model voltage mismatch caused by inverter nonlinearities and is implemented with a continuous boundary-layer adaptive-gain sliding-mode observer (ASMO) and a second-order phase-locked loop (PLL). Two-point linear prediction estimates the phase current when the VSI nonlinear voltage error actually takes effect. A C1-continuous cubic zero-crossing weight limits abrupt direction changes near current zero crossings, while a synchronous correlation signal derived from the estimated back electromotive force updates the equivalent distortion-voltage amplitude online. Compensation is applied only to the reconstructed ASMO input voltage, leaving the original current loop and space-vector pulse-width modulation (SVPWM) unchanged. Comparative and ablation simulations show lower characteristic back-EMF harmonics and electrical rotor-position estimation error than conventional compensation. Hardware tests under variable-speed and load-step conditions confirm improved dynamic estimation and disturbance rejection. The intended operating region is medium to high speed, where the back-EMF has sufficient signal-to-noise ratio and a nonsalient machine model is appropriate. The fixed-point realization is reported as implementation-feasibility evidence rather than as the principal contribution.

Machines doi: 10.3390/machines14091040

Authors: Baoting Yin He Lu Lili Dai Hongxing Ding Wenle Hu

The Newton Downhill Optimizer (NDO) combines a derivative-free downhill relation with population differences. However, its Hybrid-Guided Operator uses a random reference and persistent best-solution guidance, which can cause directional fluctuations and premature population contraction. This study proposes the Adaptive Fitness–Distance-Guided Newton Downhill Optimizer (AFDNDO). Fitness–Distance Balance selection identifies guiding individuals that account for both solution quality and spatial diversity. Stage protection, elite protection, and historical success-rate feedback regulate activation of the improved branches. A tripodal heavy-tailed update and a wave-weighted masked differential update reconstruct the two original branches. A non-uniform mutation is also triggered for low-quality individuals when the global best value stagnates. Across 30 independent runs on CEC2017, CEC2020, and CEC2022, AFDNDO attained the lowest mean rank in all six formal benchmark configurations. Its mean ranks on the 10-, 30-, and 50-dimensional CEC2017 tests were 1.172, 1.241, and 1.276, respectively. Dynamic path-planning environments included rigid obstacles, three levels of soft-risk regions, and moving obstacles. In the single-target environments, AFDNDO–DWA achieved a 100% execution success rate without collisions. In the multi-target environments, it reduced the mean objective value by 4.46–6.64% relative to NDO. It also increased the success rate from 63.33% to 70.00% in the most constrained environment. These findings indicate that AFDNDO improves cross-landscape optimization performance while retaining the basic NDO framework. They also support its use as a global planner within the tested dynamic multi-task environments.

Machines doi: 10.3390/machines14091039

Authors: Samet Yavuz Selcuk Himmetoglu

Wrist rehabilitation requires high precision, haptic transparency, and accurate alignment with the human joint’s physiological center of rotation. Conventional robotic systems often suffer from high moving inertia or joint misalignment. This paper presents the design and kinematic validation of a novel 3-DOF spherical parallel exoskeleton featuring base-fixed actuators. By mounting all actuators to a fixed base, the proposed architecture significantly reduces moving mass, achieving a low-inertia response critical for safe patient–robot interaction. The “virtual center” concept eliminates physical central joints, enabling a compact design completed by the user’s anatomy. To perform the kinematic and singularity analyses of the manipulator, two distinct models were used: Rotated Frame Based (RFB) and Initial Frame Based (IFB). Performance metrics, namely the manipulability index and condition number, are evaluated to assess dexterity and isotropy. Comparative kinematic analysis of Rotated (RFB) and Initial Frame Based (IFB) models confirm ideal central isotropy (κ=1.0). While RFB yields 94.14% high-dexterity (κ<5.0) and 99.78% usable (κ<10.0) workspace coverage, IFB achieves 78.80% high-dexterity (κ<5.0) and 92.18% usable (κ<10.0) workspace coverage. Analytical manipulability metrics validate singularity-free motion throughout the anatomical range. Additionally, the use of exponential rotational matrices in this paper provides systematic derivations of equations in compact form.

Machines doi: 10.3390/machines14091038

Authors: Yousef Farid André Preumont

This paper presents an observer-based strategy for controlling the horizontal trajectories of a hummingbird robot from on-board inertial measurements (MEMS). The centrifugal acceleration resulting from sharp turns is responsible for the dynamic coupling between the roll axis and the pitch and yaw axes. This coupling cannot be accounted for with independent control loops for the three axes; the problem can be solved with a modified state observer (MSO) introduced on the roll axis. Numerical simulations are presented to confirm the idea. The limited additional computational burden allows for real-time implementation. The MSO allows the robot to mimic the behavior of birds that lean towards the inside when turning. Under steady-state conditions (uniform longitudinal velocity and constant yaw rate), the pitch angle is such that the longitudinal component of the gravity vector balances the longitudinal drag force and the roll angle is such that the lateral component of the gravity vector balances the centrifugal acceleration.

Machines doi: 10.3390/machines14091037

Authors: Zehua Sun Yancai Xiao Haikuo Shen Shaodan Zhi

Remote fault-diagnosis services in dynamic edge–cloud environments depend on timely monitoring-data upload, remote inference, and result delivery, making their communication layer sensitive to variations in available bandwidth, link latency, and packet loss rate. This study addresses the network-state forecasting layer that supports such services rather than the fault-classification model itself. We propose Horizon-Aware Transformer–Ridge Fusion (HATR-Fusion), which combines a nonlinear Transformer expert with a low-variance ridge-regression expert for joint short- and long-horizon forecasting. Historical available bandwidth, link latency, packet loss rate, mobility, and offered load are used as inputs. Preprocessing statistics are estimated using the training set only, and validation-calibrated convex fusion weights are frozen before test inference. Experiments on controlled synthetic trajectories from eight links sampled at 1-min intervals, using 10 neural-network initialization seeds and ten baselines including DLinear and iTransformer, show that HATR-Fusion reduces mean absolute error (MAE) relative to the standalone Transformer by 6.94–8.31% over the 10-min horizon and by 3.02–4.40% over the 60-min horizon, with all six paired improvements remaining significant after Holm correction. Against iTransformer, HATR-Fusion is significantly more accurate for short-horizon bandwidth and latency, whereas iTransformer is significantly more accurate for long-horizon latency and packet loss; short-horizon packet loss and long-horizon bandwidth are not significantly different after Holm correction. The six-task mean normalized mean absolute error (NMAE) is 0.07106 for HATR-Fusion and 0.07047 for iTransformer, indicating comparable overall accuracy with task-dependent differences between the two methods. Ablation results show complementary short- and long-range contributions from ridge regression and Transformer, while input-quality sensitivity analysis identifies a limitation of the fixed fusion weights under corrupted or missing history. The conclusions are therefore restricted to scenarios with relatively stable input quality and distribution shifts comparable to those evaluated in this study.

Machines doi: 10.3390/machines14091036

Authors: Ugo Roux Saeed Saeedvand Jacky Baltes

We present a reward-free control framework for balancing and steering a two-wheeled scooter using a diffusion-based world model. Rather than engineering a reward, we specify goals directly in observation space: target values (e.g., zero roll and zero yaw error) are pinned through a continuous mask, and classifier-free guidance amplifies the goal signal during trajectory generation. Because the mask is continuous at inference, goals can be traded off online (for instance, relaxing the balance constraint during sharp turns to allow necessary leaning) without retraining. The model is a FiLM-Mixer denoising network trained with V-prediction diffusion. At deployment, the controller runs in real time using a single diffusion step with warm-started predictions. We validate the approach on a full-sized Thormang3 humanoid operating a Gogoro Viva scooter in simulation, and deploy it on physical hardware. It matches a PPO baseline tuned with six reward components on balance, survival, and heading tracking while producing smoother commands, all without the per-task reward-shaping step. Diffusion training introduces its own loss-weight hyperparameters; unlike reward weights, however, these are task-agnostic. They govern the denoising procedure rather than the desired behavior, and are therefore set once and reused unchanged across goals rather than re-tuned for each new task. Because the model learns to predict trajectories rather than to maximize a reward, its training signal depends only on observed states and actions, not on reward labels. Real hardware recordings can therefore be folded directly into the same loss, providing a route toward closing the sim-to-real gap that reward-based methods such as PPO structurally cannot use.

Machines doi: 10.3390/machines14091035

Authors: Xiaoman Li Li Zu Yang Xu Haonan Cai Qizhi Wang Haoran Jin Huangkai He

Planetary roller screw mechanisms (PRSMs) are critical transmission components in high-end equipment and precision electromechanical systems, and their travel error directly affects motion accuracy and operational stability. This review systematically examines the main sources and research progress of PRSM travel error, including manufacturing and geometric errors, installation errors, load and thermal deformation errors, and service and environmental factors. It further reviews the current state of research on travel error models, with emphasis on travel error modeling and dominant error identification. Existing measurement methods are classified into static measurement, dynamic measurement based on linear encoders or laser interferometers, machine vision measurement, and auxiliary methods for specific operating conditions, and are compared in terms of accuracy, applicable test objects, and engineering limitations. The review shows that current studies have established an important basis for travel error prediction, compensation, and test system development, but limitations remain in experimental validation, dedicated test rigs, component-level measurement of roller external raceways and nut internal raceways, and unified evaluation standards. Future research should develop specialized measurement systems that consider multi-contact characteristics, representative loading conditions, backlash elimination, error separation, and data compensation to support high-precision PRSM applications.

Machines doi: 10.3390/machines14091034

Authors: Jinbao Zhao Hanyun Zhang Guolin He Fulei Zhang

At present, construction robots have received extensive attention in construction scenarios such as wall masonry and component handling. The operational reliability of their joint transmission systems directly affects the working accuracy and construction safety of robots. To address the problem that planetary reducers in construction robots are prone to faults under complex working conditions, this paper proposes a fault-detection method based on an adaptive observer. First, a state–space model containing unknown disturbances and actuator faults is established by combining the transmission structure and typical fault mechanism of the planetary reducer. On this basis, an adaptive observer with more degrees of freedom is designed, and the L∞ disturbance-robustness index is introduced. The augmented-output function is used to apply the H− performance index to enhance fault sensitivity. The simulation results show that when the system is fault-free, the residual remains below the threshold, and no false alarm occurs. After a time-varying crack fault is introduced, the residual can quickly exceed the threshold and trigger a complete fault alarm. Compared with the L∞ observer, the introduced H− performance index significantly improves the fault sensitivity of the observer. Compared with the traditional Luenberger observer, the proposed method has better disturbance robustness and fault sensitivity, providing an effective method for early fault detection of planetary reducers in construction robots.

Machines doi: 10.3390/machines14091033

Authors: A. Abeena N. Praveen Kumar

Interior Permanent Magnet Synchronous Motors (IPMSMs) are widely used in traction and industrial drive systems because they combine high efficiency, high power density, and excellent performance over a wide speed range. Rotor eccentricity, however, remains one of the most significant faults affecting their reliable operation; diagnosing it early is crucial for avoiding unexpected breakdowns. To achieve this, the analysis utilizes a simulation-based fault diagnosis framework that combines the Finite Element Method (FEM) with machine learning. A 550 W, 220 V IPMSM was modeled in ANSYS Maxwell to simulate dynamic eccentricity faults at three severity levels: 10%, 20%, and 40%. A fixed-length, non-overlapping window segmentation approach was used to pull statistical features from the stator current and radial air-gap flux density signals. These features were then fed into several supervised machine learning algorithms, evaluated using a consistent 5-fold cross-validation protocol across all investigated classifiers, with the Ensemble Bagged Trees classifier achieving validation accuracies of 93.12% for radial air-gap flux density and 87.86% for stator current. By integrating finite-element analysis, statistical feature extraction, and comparative machine learning, the proposed framework demonstrates the feasibility of simulation-based dynamic eccentricity severity classification in IPMSMs. The results indicate that Ensemble Bagged Trees provide the best classification performance among the evaluated classifiers, establishing a foundation for future experimental validation and real-time condition-monitoring applications.

Machines doi: 10.3390/machines14091032

Authors: Taehong Min Joonghyeok Lee

Compound faults in electric motors are difficult to diagnose because simultaneous-fault recordings are scarce and fault multiplicity is unknown at inference. We propose a compound-sample-free, cardinality-free framework for induction motors running continuously varying speed profiles at several load levels. Synchronized key-phase, triaxial vibration and three-phase current signals are converted to order-domain representations by anti-aliased computed order tracking, augmented by a band-pass envelope order spectrum and normalized with a scale-invariant, noise-floor-removed representation. Nine fault primitives are evaluated by modality-specific experts, combined through physics-regularized routing, and decoded by maximum a posteriori inference over 19 feasible machine states. Six leave-one-speed-load-combination-out folds and three seeds evaluate every held-out recording. Exact condition accuracy counts a recording as correct only when the predicted set of fault primitives matches the true set exactly; exact compound recovery applies the same criterion to the unseen compound recordings, requiring both constituent primitives and nothing else. Without a fault-count prior, the pipeline reaches 76.7% and 45.7%, against 62.5% and 11.1% for a conventional order-domain front end and 59.2–62.0% and 0.0–2.5% for three re-implemented baselines. A source-domain modality-selection control reduces compound recovery from 59.3% to 27.8%. Physically aligned representation, sensing specialization and cardinality-aware decoding are therefore critical to compositional motor-fault diagnosis.

Machines doi: 10.3390/machines14091031

Authors: Hansol Kang Hyunyong Lee Jiman Park Seongwon Nam Yeongwoo Son Bumsu Yi Jaeyoung Oh Hyeonwoo Yu Hyouk Ryeol Choi

Reinforcement learning (RL) has become a powerful tool for quadrupedal locomotion, and a sim-to-real approach is widely adopted to avoid hardware damage during training. However, the “sim-to-real gap” remains a critical challenge, particularly for robots driven by high-gear-ratio actuators, in which nonlinear friction effects are strongly amplified. Conventional methods, such as actuator networks or heuristic domain randomization, often require specialized sensors or extensive trial-and-error to tune appropriate randomization ranges. Building on a recent system-identification framework for actuator dynamics, we extend it with an augmented friction model that incorporates the Stribeck effect to capture the low-velocity nonlinearities characteristic of high-gear-ratio actuators. The physical parameters are identified from real-robot trajectory data using an evolutionary algorithm, and the resulting simulation is used to train a locomotion policy that is transferred zero-shot to a 55 kg quadruped without additional fine-tuning or base- or controller-level dynamics randomization. On our platform, adding the Stribeck term lowers the actuator identification error by 16% relative to a Coulomb–Viscous model on the trajectory used for identification, and this advantage generalizes to an unseen trajectory not used for identification. It also lowers the simulation-to-reality mean-velocity degradation from 40.2% and 26.8% for the Coulomb–Viscous model to 27.2% and 21.6% for our method at the 0.3 and 1.0 m/s commands, respectively. The trained policy achieves stable locomotion on flat ground as well as rough terrain including steps and stairs. These results indicate that explicitly modeling low-velocity friction is beneficial for high-fidelity sim-to-real transfer in high-reduction systems.

Machines doi: 10.3390/machines14091029

Authors: Mercè Garcia-Vilchez Robert Castilla Arne Hauschildt Carmen Carrillo Pedro Javier Gamez-Montero

A water–steam ejector can be used for steam exhausting in some equipment, such as sterilization systems in hospital facilities. In this type of ejector, a water jet in the center is used to entrain and transport steam from the sterilization chamber. The performance of the ejector, which is a function of the pressure in the sterilization chamber and the temperature of the water, is crucial for the reduction in water and energy consumption. The numerical simulation of this type of device is challenging because of the mass, momentum and energy exchange between the phases. In this study, numerical simulations of an axisymmetrical model of a water–steam ejector considering mixing and heat and mass transfer by condensation are shown. The Euler–Euler method with OpenFOAM v2312 was used. The influence of the steam-bubble diameter was studied, and the results were validated with a previously published experimental report, which showed an agreement of approximately 2.5% for the adopted mesh size. The proposed solution may be appropriate for rapid design procedures for these types of devices.

Machines doi: 10.3390/machines14091030

Authors: Lei Song Chuanhao Zheng Shengkai Zhao Qin Bie Zhixiang Dai Feng Wang Jinjie Zhang Jiachen Liu

Remaining useful life (RUL) prediction of wind-turbine bearings is challenged by nonstationary wind loads, multistage degradation, substantial lifetime dispersion, and strict deployment constraints. Conventional single-task regressors apply a unified feature-to-RUL mapping over the entire life cycle and therefore struggle to characterize stage transitions and bearing-specific degradation progress. To address these issues, this paper proposes a stage-aware multi-task RUL prediction method with causal degradation-prior fusion and collaborative distillation. During training, a high-capacity reference representation path transfers inter-sample relational structures and task-level degradation knowledge to a compact feature encoding path, while only the compact path is retained for inference. Based on the compact representation, a multi-task module jointly performs four-stage classification, stage-conditioned RUL regression, and continuous remaining-life estimation; predicted stage probabilities softly fuse the stage-conditioned outputs. A causal prior-fusion module further integrates a bearing-specific healthy-state anchor, causally identified first prediction time, cumulative damage, and the stage-aware prediction to adapt the RUL trajectory to individual degradation processes. Experiments on the IEEE PHM 2012 and XJTU-SY datasets demonstrate that the proposed method provides accurate and robust RUL prediction across different bearing degradation processes. Moreover, the compact inference path maintains efficient implementation, supporting its potential use in practical wind-turbine condition-monitoring applications.

Machines doi: 10.3390/machines14091028

Authors: Zhiming Xiao Chi Chen Zhihao Ke

Micro electrical discharge deposition (micro-EDD) enables maskless direct-write metallic tracks on conductive substrates, but reported implementations rely on purpose-built machines with programmable pulse generators and gap servos. This paper reports a low-cost micro-EDD platform retrofitted from a desktop CNC router driven by GRBL firmware, extended with an Android control application for path definition and process supervision. Discharge is produced by an RC relaxation circuit with no pulse generator and no gap feedback. Copper, aluminium, nickel, and titanium electrodes were deposited onto silicon substrates in an argon atmosphere, maintaining a 10 μm separation. The working point, pd ≈ 0.76 Torr·cm, lies within 20% of the argon Paschen minimum. Track width rises linearly with supply voltage over 330–400 V (w = 0.570 V − 99.2 μm, R2 = 0.994), extrapolating to a deposition threshold of 174 V, 37 V above the argon breakdown minimum—indicating an energy threshold distinct from breakdown. Across electrode materials, width shows a trend consistent with thermal diffusivity as w∝α0.46 (R2 = 0.69, N = 4), resolving the inconsistency that copper, the best conductor, gives the widest track. Polarity reversal changes width by factors of 4.6 (Cu) and 7.5 (Al), consistent with anode-dominated energy partition. Péclet numbers remain below 3 × 10−4 and overlap ratios above 103, excluding thermal advection and insufficient overlap as causes of the feed-rate collapse. A five-axis kinematic extension is derived, with Z travel identified as the bounding constraint.

Machines doi: 10.3390/machines14091027

Authors: Sihui Dong Yuebiao Zhao Xingyu Zhou Wenhao Bai

Lightweight design of high-speed-train water tanks can easily lower the natural frequency of baffles, increasing the risk of resonance and fatigue failure and thus threatening operational safety. To address this issue, a lightweight optimization method that does not reduce the first-order natural frequency of the baffle is proposed, taking a suspended water tank of a certain type of CRH electric multiple unit (EMU) as the research object. First, a two-way fluid–structure interaction (FSI) finite element model was established based on the computational fluid dynamics (CFD) method. The design of experiments method was employed to determine the optimal number of baffles inside the tank, and the response surface methodology was applied to construct quadratic polynomial surrogate models for baffle mass, maximum water tank stress, baffle deformation, and the first-order natural frequency. Analysis of variance was conducted to verify the fitting accuracy and significance of each model. After clarifying the influence of design variables on the response indicators, and to overcome the shortcomings of the standard multi-objective particle swarm optimization (MOPSO) algorithm, such as susceptibility to local optima, simplistic constraint handling, and premature convergence, an improved multi-objective particle swarm optimization (IMOPSO) algorithm integrating chaotic initialization, adaptive parameter adjustment, and a dynamic mutation strategy was proposed. With the first-order natural frequency serving as a constraint, multi-objective optimization of the baffle structure was carried out. Finally, the prediction accuracy of the surrogate models was numerically validated using finite element simulation software. The results show that after optimization, the baffle mass was reduced by 16.13%, the first-order natural frequency increased by 0.41 Hz (by FEM), the maximum water tank stress decreased by 288 Pa (by FEM), and the baffle deformation was reduced by 7.66% according to FEM verification (RSM surrogate-model prediction gave a reduction of 9.07%). The maximum prediction error of the surrogate models was only 1.53%, confirming the effectiveness and feasibility of the proposed method. This study can provide a theoretical basis and engineering reference for the improvement and performance optimization of water tanks on CRH EMUs and other similar tank structures.

Machines doi: 10.3390/machines14091026

Authors: Qiguang Shen Zhaoyue Wang Yifei Feng Kun Xu

Mapless navigation often removes global maps while retaining localization-derived goal vectors or bearings. We study a stricter setting in which a mobile robot observes only local LiDAR, scalar goal range, and short histories of executed actions; neither pose nor goal direction is provided to the policy. We introduce ACR-Nav, an action-conditioned range navigation framework that converts scalar-range evolution into closed-loop progress information. Its range–action history associates each distance change with the motion that produced it, while the sectorized LiDAR captures local geometry and short-term obstacle motion. A LiDAR-only safety filter provides immediate collision intervention, and a static-to-mixed curriculum stabilizes learning. A lightweight multilayer–perceptron is optimized with Proximal Policy Optimization (PPO), while the ACR-Nav formulation itself remains optimizer-agnostic. In corridor simulations, ACR-Nav achieved 93.2%, 80.4%, and 84.4% success in static, mixed, and dynamic environments. Removing the safety filter reduced success by 15.2, 14.6, and 16.0 percentage points in static, mixed, and dynamic environments, respectively, and random-goal tests yielded 91.2% and 81.4% success in static and mixed settings. Topology-shift experiments further quantified adaptation to an L-shaped corridor. The results show that action-conditioned scalar-range evolution can support goal-directed, segment-level navigation within locally straight corridor passages without exposing robot pose or target bearing to the policy.

Machines doi: 10.3390/machines14091025

Authors: Alex Ueberbacher Andreas Auer Stefan Sendlbeck Michael Otto Karsten Stahl

The increasing performance requirements of electric vehicle powertrains demand lightweight, efficient, and low-noise transmission systems. High-speed electric drive unit concepts offer significant potential for reducing motor size and mass by shifting torque generation to higher rotational speeds. However, this approach places increased demands on gearbox power density, efficiency, and noise, vibration, and harshness (NVH) performance. This work investigates the NVH behaviour of a compact, high-speed automotive gearbox with a focus on planetary gear stages. Although planetary stages offer high compactness, their complex kinematics can lead to pronounced NVH challenges. In particular, sequentially phased gear meshing results in characteristic sideband components whose orders can be predicted analytically, while their amplitudes remain difficult to estimate reliably during the design phase, necessitating experimental validation. Several NVH-oriented design measures, including high-contact-ratio gearing and low-NVH microgeometry, are applied to a two-stage gearbox comprising a planetary and a cylindrical gear stage. Peak-to-peak transmission error is used as a primary NVH design metric. The planetary stage is analysed in detail to assess the influence of sequential phasing on sideband components in the dynamic response and resulting vibration behaviour. The NVH-oriented gearbox is tested on a bench, with housing accelerations used to analyse planetary sidebands, providing insights into the NVH potential of compact, high-speed gearboxes and the role of sequential phasing in the vibration response.

Machines doi: 10.3390/machines14091024

Authors: DongHee Park JaeGwang Yoon ByeongKeun Choi

This study compares a conventional feature-based representation and a physics-guided condition-index (CI) representation for vibration-based fault diagnosis of industrial motors. Both representations were constructed from identical vibration signal segments and evaluated under the same classification conditions using support vector machine (SVM) classifiers. For each representation, a genetic algorithm (GA) was repeatedly applied to the training data to select three representative variables, after which the SVM hyperparameters were optimized using three-fold cross-validation. Permutation Importance and SHapley Additive exPlanations (SHAP) were subsequently used to interpret the contributions of the selected CIs. Independent test motors, whose fault conditions had been established through manufacturer troubleshooting before the present analysis, were excluded from all model-development procedures. For the independent Unbalance and Misalignment test motors, the CI-based model achieved segment-level classification rates of 99.83% and 100%, respectively, whereas the conventional representation showed substantial misclassification. Because these segments originated from a single physical motor for each fault condition, the reported rates represent within-motor segment-level outcomes rather than population-level estimates of diagnostic performance. FFT analysis revealed dominant 1X and 2X components in the corresponding test data, consistent with their established fault conditions. For an additional independent motor identified as Air-gap Unbalance, the CI-based model classified all test segments as Air-gap Unbalance, while the FFT spectrum exhibited fractional-frequency characteristics similar to those observed in the corresponding fault data. Overall, the physics-guided CI representation produced classification outcomes that were more consistent with the established fault conditions and provided a more physically interpretable basis for model decisions under the industrial motor conditions examined in this study.