Machines

19 pages, 9282 KB

Open AccessArticle

Analysis of Sound Insulation Performance in Aeronautical Composite Materials and Optimization Study on Film Metamaterials

by Chenying Hu, Yu Ning and Jintao Gu

Machines 2026, 14(9), 1042; https://doi.org/10.3390/machines14091042 - 14 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Vibration Energy Harvesting and Intelligent Monitoring for Rotating Machinery)

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25 pages, 8979 KB

Open AccessArticle

A Delay-Aware Method for Inverter Nonlinearity Compensation in Sensorless PMSM Drives

by Wenyu Zhao, Zhenguo Gao, Yuhui Yang, Yuanxiang Guo, Zhijue Huang, Peng Zhao and Xueshan Gao

Machines 2026, 14(9), 1041; https://doi.org/10.3390/machines14091041 - 14 Sep 2026

Abstract

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 [...] Read more.

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

C^{1}

-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. Full article

(This article belongs to the Special Issue Advanced Sensorless Control of Electrical Machines)

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41 pages, 7388 KB

Open AccessArticle

Adaptive Fitness–Distance-Guided Newton Downhill Optimizer for Dynamic Multi-Target Path Planning

by Baoting Yin, He Lu, Lili Dai, Hongxing Ding and Wenle Hu

Machines 2026, 14(9), 1040; https://doi.org/10.3390/machines14091040 - 12 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Automation and Control Systems)

38 pages, 7856 KB

Open AccessArticle

A Novel Parallel Exoskeleton for Wrist Rehabilitation: Conceptual Design, Kinematics, and Singularity Analysis

by Samet Yavuz and Selcuk Himmetoglu

Machines 2026, 14(9), 1039; https://doi.org/10.3390/machines14091039 - 12 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue New Advances in Science of Mechanisms and Machines)

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18 pages, 5914 KB

Open AccessArticle

Observer-Based Control of Hummingbird Robot Trajectories

by Yousef Farid and André Preumont

Machines 2026, 14(9), 1038; https://doi.org/10.3390/machines14091038 - 11 Sep 2026

Abstract

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. [...] Read more.

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. Full article

(This article belongs to the Special Issue The Kinematics and Dynamics of Mechanisms and Robots)

33 pages, 12143 KB

Open AccessArticle

Ensemble Network-State Forecasting for Remote Fault Diagnosis Using Transformer and Ridge Regression

by Zehua Sun, Yancai Xiao, Haikuo Shen and Shaodan Zhi

Machines 2026, 14(9), 1037; https://doi.org/10.3390/machines14091037 - 11 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis)

37 pages, 6361 KB

Open AccessArticle

Reward-Free Scooter Balance Control via Diffusion World Models with Goal-Conditioned Trajectory Generation

by Ugo Roux, Saeed Saeedvand and Jacky Baltes

Machines 2026, 14(9), 1036; https://doi.org/10.3390/machines14091036 - 11 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Automation and Control Systems)

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34 pages, 6991 KB

Open AccessReview

A Review of Research on Travel Error in Planetary Roller Screw Mechanisms

by Xiaoman Li, Li Zu, Yang Xu, Haonan Cai, Qizhi Wang, Haoran Jin and Huangkai He

Machines 2026, 14(9), 1035; https://doi.org/10.3390/machines14091035 - 11 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advances in Precision Mechanical Transmission Systems: Design, Dynamics, and Applications)

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21 pages, 4307 KB

Open AccessArticle

Research on Gear Fault Detection of Planetary Reducers for Construction Robots Based on an Adaptive Observer

by Jinbao Zhao, Hanyun Zhang, Guolin He and Fulei Zhang

Machines 2026, 14(9), 1034; https://doi.org/10.3390/machines14091034 - 11 Sep 2026

Abstract

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 [...] Read more.

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_{\infty}

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_{\infty}

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. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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25 pages, 12972 KB

Open AccessArticle

An FEM-Informed Statistical Feature Extraction and Comparative Machine Learning Framework for Dynamic Eccentricity Fault Diagnosis in Interior Permanent Magnet Synchronous Motors

by A. Abeena and N. Praveen Kumar

Machines 2026, 14(9), 1033; https://doi.org/10.3390/machines14091033 - 10 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Electrical Machines and Drives)

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29 pages, 4529 KB

Open AccessArticle

Physics-Guided Compositional Diagnosis of Unseen Compound Faults in Variable-Speed Induction Motors

by Taehong Min and Joonghyeok Lee

Machines 2026, 14(9), 1032; https://doi.org/10.3390/machines14091032 - 10 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Fault Detection in Induction Motors)

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20 pages, 4604 KB

Open AccessArticle

Enhancing Sim-to-Real Transfer for a High-Gear-Ratio Quadruped Robot via Extended Actuator Dynamics Identification

by Hansol Kang, Hyunyong Lee, Jiman Park, Seongwon Nam, Yeongwoo Son, Bumsu Yi, Jaeyoung Oh, Hyeonwoo Yu and Hyouk Ryeol Choi

Machines 2026, 14(9), 1031; https://doi.org/10.3390/machines14091031 - 9 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue The Future of Mobility: Exploring Wheeled–Legged Robot Systems)

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24 pages, 4511 KB

Open AccessArticle

Stage-Aware Multi-Task Learning with Causal Degradation-Prior Fusion for Remaining Useful Life Prediction

by Lei Song, Chuanhao Zheng, Shengkai Zhao, Qin Bie, Zhixiang Dai, Feng Wang, Jinjie Zhang and Jiachen Liu

Machines 2026, 14(9), 1030; https://doi.org/10.3390/machines14091030 - 9 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)

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16 pages, 2646 KB

Open AccessArticle

A Numerical Study of a Water–Steam Ejector for Steam Exhausting with OpenFoam—The Effect of Steam-Bubble Diameter

by Mercè Garcia-Vilchez, Robert Castilla, Arne Hauschildt, Carmen Carrillo and Pedro Javier Gamez-Montero

Machines 2026, 14(9), 1029; https://doi.org/10.3390/machines14091029 - 9 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Turbomachinery)

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35 pages, 4851 KB

Open AccessArticle

A Low-Cost Retrofitted CNC Platform with Mobile-Terminal Control for Micro Electrical Discharge Deposition: System Design and Process Characterization

by Zhiming Xiao, Chi Chen and Zhihao Ke

Machines 2026, 14(9), 1028; https://doi.org/10.3390/machines14091028 - 8 Sep 2026

Abstract

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 [...] Read more.

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 \propto α^{0.46}

(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. Full article

(This article belongs to the Section Advanced Manufacturing)

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28 pages, 27477 KB

Open AccessArticle

Lightweight Design of the Baffle Structure in a High-Speed-Train Water Tank Based on an Improved Multi-Objective Particle Swarm Optimization Algorithm

by Sihui Dong, Yuebiao Zhao, Xingyu Zhou and Wenhao Bai

Machines 2026, 14(9), 1027; https://doi.org/10.3390/machines14091027 - 8 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Vehicle Engineering)

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28 pages, 1800 KB

Open AccessArticle

ACR-Nav: Localization-Free Corridor Navigation via Action-Conditioned Scalar-Range Evolution

by Qiguang Shen, Zhaoyue Wang, Yifei Feng and Kun Xu

Machines 2026, 14(9), 1026; https://doi.org/10.3390/machines14091026 - 8 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advanced Planning, Perception, and Control for Autonomous Vehicles and Robots)

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22 pages, 4273 KB

Open AccessArticle

Example Case of a High-Speed Gearbox Concept for E-Mobility with a Sequentially Phased Planetary Stage Focusing on NVH Measurements

by Alex Ueberbacher, Andreas Auer, Stefan Sendlbeck, Michael Otto and Karsten Stahl

Machines 2026, 14(9), 1025; https://doi.org/10.3390/machines14091025 - 8 Sep 2026

Abstract

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 [...] Read more.

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. Full article

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17 pages, 2250 KB

Open AccessArticle

Comparative Evaluation of Conventional Feature-Based and Physics-Guided Condition-Index Representations for Industrial Motor Fault Diagnosis

by DongHee Park, JaeGwang Yoon and ByeongKeun Choi

Machines 2026, 14(9), 1024; https://doi.org/10.3390/machines14091024 - 8 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Electrical Machines and Drives)

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