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Volume 14
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Machines, Volume 14, Issue 6 (June 2026) – 132 articles

Cover Story (view full-size image): Digital twins are increasingly deployed in smart manufacturing, but their computational demands can compromise real-time control. This study provides the first quantitative mapping between controller complexity and achievable real-time class in digital twin-based process control. Three architectures (PID, Model Predictive Control (MPC), and Robust MPC) are benchmarked using a finite-difference thermal model of a steel slab subjected to boundary heat flux. PID satisfies hard real-time constraints, while MPC meets soft real-time requirements with zero deadline misses. Robust MPC falls into the near-real-time class, operating at 154× the control period. MPC also tolerates actuation delays that destabilize simpler controllers, making it the preferred choice when digital twin latency is unavoidable. View this paper
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21 pages, 4536 KB  
Article
Partial Discharge Severity Classification for Transformer Condition Monitoring Using Feature Engineering, PCA, and ANN
by Lucas Thobejane and Bonginkosi A. Thango
Machines 2026, 14(6), 711; https://doi.org/10.3390/machines14060711 - 22 Jun 2026
Viewed by 249
Abstract
Partial discharge (PD) is a key indicator of insulation degradation in high-voltage transformers and can provide early warning of incipient failure. Although artificial neural networks (ANNs) have been applied to PD classification, their performance may be affected by redundant features and overfitting when [...] Read more.
Partial discharge (PD) is a key indicator of insulation degradation in high-voltage transformers and can provide early warning of incipient failure. Although artificial neural networks (ANNs) have been applied to PD classification, their performance may be affected by redundant features and overfitting when using expanded feature spaces. This study proposes a PD severity classification framework that combines physics-informed feature engineering, principal component analysis (PCA), and a multilayer perceptron (MLP) neural network. PD measurements were acquired from a physical transformer using the IEC 60270 electrical measurement method, yielding 294 samples labelled into four severity classes: normal, low, medium, and high PD. Two measured variables, namely PD magnitude and applied voltage, were expanded into a 10-dimensional feature space using energy-based, ratio-based, logarithmic, and normalized features. PCA was then used to reduce the feature space, and the retained principal components were used as inputs to the classifier. The results show that the first two principal components captured more than 90% of the total variance and enabled the MLP to achieve 98.3% test accuracy, matching the performance obtained using all 10 engineered features and improving on classification based on the raw measurements alone (91.5%). The proposed PCA-ANN model also achieved perfect precision and recall for the medium- and high-severity classes on the test set, and outperformed K-nearest neighbours, support vector machine, and Gaussian Naïve Bayes models in 5-fold cross-validation. These findings indicate that PCA can reduce feature dimensionality without loss of diagnostic performance, providing an efficient approach for transformer PD severity classification. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis)
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21 pages, 2163 KB  
Article
A Short-Circuit Fault Diagnosis Method for Three-Phase Current-Source Inverters Using Normalized Phase Current Variation Trends
by Junhao Zhan, Jixin Wang, Naizhe Diao and Xianrui Sun
Machines 2026, 14(6), 710; https://doi.org/10.3390/machines14060710 - 22 Jun 2026
Viewed by 200
Abstract
This paper presents a fast diagnosis and localization method for switch short-circuit faults (shoot-through faults) in three-phase current-source inverters (CSIs) based on the polarity and variation trends of normalized phase currents. Under short-circuit fault conditions, the variation trends of the two same-polarity phase [...] Read more.
This paper presents a fast diagnosis and localization method for switch short-circuit faults (shoot-through faults) in three-phase current-source inverters (CSIs) based on the polarity and variation trends of normalized phase currents. Under short-circuit fault conditions, the variation trends of the two same-polarity phase currents change from opposite (normal) to identical. To capture this feature, an adaptive magnitude-normalization method is proposed, which adaptively distinguishes normal load variations from fault conditions and selects the corresponding normalization strategy, yielding constant-amplitude three-phase currents while retaining polarities and trends. The theoretical operating sector is determined from the current polarities, and the faulty switch is localized using the signs of the variation trends of the two same-polarity currents. The method applies to both single- and multiple-switch faults. Experiments on a 3 A, 50 Hz CSI prototype show an average localization time of 15 ms (0.75Tbase), accurate diagnosis under load (10–30 Ω) and frequency (25–50 Hz) variations, and no need for additional hardware, confirming its effectiveness. Full article
(This article belongs to the Special Issue Advanced Control and Fault Diagnosis in Electrical Drives)
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28 pages, 10680 KB  
Article
Intelligent Mapping and Control of Stresses in a Hydraulic Materials Handling Crane
by Appiah-Osei Agyemang, Sasu Mäkinen and Daniel Roozbahani
Machines 2026, 14(6), 709; https://doi.org/10.3390/machines14060709 - 21 Jun 2026
Viewed by 186
Abstract
The objective of this research was to develop an intelligent stress mapping and a smart control platform, utilizing Artificial Intelligence (AI), to increase the fatigue life of a hydraulic crane. The crane’s boom was modeled and co-simulated using ANSYS, ADAMS, and MATLAB. A [...] Read more.
The objective of this research was to develop an intelligent stress mapping and a smart control platform, utilizing Artificial Intelligence (AI), to increase the fatigue life of a hydraulic crane. The crane’s boom was modeled and co-simulated using ANSYS, ADAMS, and MATLAB. A flexible model of the boom was created in ANSYS and then exported to ADAMS. Stress analysis was performed using the maximum principal hotspot method and the von Mises yield criterion. Stress optimization was conducted using a Neural Network (NN) algorithm, which is a key implementation of AI in this study. Two control platforms, one based on Neural Networks and another on Fuzzy Logic, were designed to apply AI in controlling the crane’s movements. The Neural Network algorithm optimized the crane’s movement by adjusting velocity at critical positions where structural stress was high, while the fuzzy logic-based control algorithm utilized stress feedback from the crane’s structure. Both AI-driven control algorithms were integrated into the physical crane in the lab, and extensive testing demonstrated a significant increase in the crane’s fatigue life, along with effective damping of crane vibrations. This paper introduces a novel AI-driven approach combining Neural Networks and Fuzzy Logic for intelligent stress mapping and control, specifically tailored for hydraulic cranes. Unlike previous works, this research integrates real-time stress feedback into the control process and validates the algorithms through experimental implementation on a prototype crane, significantly improving its fatigue life. Full article
(This article belongs to the Special Issue Artificial Intelligence and Robotics in Manufacturing and Automation)
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20 pages, 4211 KB  
Article
On the Role of Feature Extraction in Transformer PD Severity Classification: A Controlled Comparison of PCA and Autoencoder Models
by Lucas Thobejane and Bonginkosi Thango
Machines 2026, 14(6), 708; https://doi.org/10.3390/machines14060708 - 21 Jun 2026
Viewed by 253
Abstract
This paper applies the comparative PCA-ANN vs. Autoencoder-ANN framework to transformer partial discharge (PD) severity classification, using a 294-sample dataset spanning four severity classes: Normal, Low PD, Medium PD, and High PD. Two raw measurements of discharge magnitude (pC) and applied voltage (kV) [...] Read more.
This paper applies the comparative PCA-ANN vs. Autoencoder-ANN framework to transformer partial discharge (PD) severity classification, using a 294-sample dataset spanning four severity classes: Normal, Low PD, Medium PD, and High PD. Two raw measurements of discharge magnitude (pC) and applied voltage (kV) are expanded into a 15-dimensional physics-informed feature space. Both linear (PCA) and nonlinear (bottleneck Autoencoder) feature extraction are evaluated exhaustively across all latent dimensions k = 1–15, feeding an identical ANN classifier. PCA + ANN achieves perfect test accuracy of 100.0% at k = 9, while Autoencoder + ANN achieves 98.3% at k = 8. PCA + ANN demonstrates superior performance on this dataset, attributed to the low intrinsic dimensionality of the two-measurement PD feature space and the highly separable nature of PD severity classes in the engineered ratio feature space. The Autoencoder provides a more compact latent representation but introduces classification errors for the Normal class due to its extreme under-representation. Cross-validation confirms PCA + ANN stability (97.4 ± 0.9% vs. 97.0 ± 1.0%). These results, alongside the companion DGA study, provide the complete baseline for comparing linear and nonlinear feature extraction across two transformer diagnostic modalities. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis)
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18 pages, 9710 KB  
Article
MOPSO-Based Design Optimization for Armature Coils in High-Propulsive-Force Electrodynamic Vibrators
by Xiaohong Fu, Minggang Zhu, Jianping Shen and Zhigang Liu
Machines 2026, 14(6), 707; https://doi.org/10.3390/machines14060707 - 20 Jun 2026
Viewed by 242
Abstract
Directly coupled electrodynamic vibrators are widely used in vibration testing due to their ability to generate large propulsive forces. However, increasing the propulsive force typically requires higher driving currents, which leads to significant electrical heat generation and thermal management challenges in the armature [...] Read more.
Directly coupled electrodynamic vibrators are widely used in vibration testing due to their ability to generate large propulsive forces. However, increasing the propulsive force typically requires higher driving currents, which leads to significant electrical heat generation and thermal management challenges in the armature coil. To address this issue, this study proposes a multi-objective parameter optimization framework for the design of armature coils in high-propulsive-force electrodynamic vibration tables. Two optimization objectives are formulated based on electromagnetic and thermal considerations: minimization of electrical heat generation in the armature coil; and improvement in cooling capability, characterized by the ratio between the cooling water channel area and the conductive cross-sectional area. The key geometric parameters of the coil, including winding configuration and cross-sectional dimensions, are treated as design variables. The resulting multi-objective optimization problem is solved using a multi-objective particle swarm optimization (MOPSO) algorithm to obtain a set of Pareto-optimal solutions that balance the two competing thermal objectives. The present work focuses on the pre-design-stage optimization of the armature coil after the rated propulsive force and geometric envelope of the vibrator have been specified. A representative high-propulsive-force electrodynamic vibrator is analyzed as a case study. Finite element thermal simulations show that the selected Pareto-optimal design reduces the peak armature-coil temperature by approximately 9.7–36.6% compared with the other investigated coil configurations under the same propulsive force condition. The proposed method provides an efficient approach for the thermally constrained parameter design of high-power electrodynamic vibrator armature coils. Full article
(This article belongs to the Section Machine Design and Theory)
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17 pages, 5585 KB  
Article
Identification and Elimination of Blade-Root Fillet Overcutting Interference for Integral Impeller Plunge Milling
by Xueqin Wang, Mingqian Guo, Jianning Zhu, Zhaocheng Wei and Jingyang Feng
Machines 2026, 14(6), 706; https://doi.org/10.3390/machines14060706 - 20 Jun 2026
Viewed by 241
Abstract
As a prominent high-efficiency metal cutting process, plunge milling has found increasing applications in the rough machining of integral impellers. However, challenges arise due to the time-consuming process of avoiding interference-induced overcutting at the blade-root fillet, leading to excessive residual material. Consequently, the [...] Read more.
As a prominent high-efficiency metal cutting process, plunge milling has found increasing applications in the rough machining of integral impellers. However, challenges arise due to the time-consuming process of avoiding interference-induced overcutting at the blade-root fillet, leading to excessive residual material. Consequently, the full potential of plunge milling’s high-efficiency advantages is constrained. To address these issues, a method to avoid overcutting caused by cutter interference at the blade-root fillet in integral impeller plunge milling is proposed. First, a parameterized model of the blade-root fillet is established using a rolling ball model. Second, a semi-analytical model for identifying cutter interference at the blade-root fillet is established through micro-element discretization. Lastly, the cutter position is adjusted along the direction of the vertical cutter axis vector to avoid overcutting. The modeling error of the blade-root fillet remains within 0.1%, ensuring high accuracy in overcut detection. Furthermore, the identification process is completed in less than 1s, demonstrating its computational efficiency. Compared with the conventional depth-reduction method, the proposed interference elimination strategy reduces the excessive residual material volume by 66% while avoiding overcutting, with only a 26% increase in plunge roughing time. Simulation and experimental validation on an 820 mm diameter impeller confirm the method’s effectiveness in balancing interference avoidance and material removal efficiency. Full article
(This article belongs to the Section Advanced Manufacturing)
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18 pages, 4749 KB  
Article
Tooth Root Crack Propagation: A Method to Convert Pulsator Experimental Lifetime to Meshing Conditions
by Lorenzo Valsecchi, Luca Bonaiti, Sergio Sartori, Michael Geitner and Carlo Gorla
Machines 2026, 14(6), 705; https://doi.org/10.3390/machines14060705 - 20 Jun 2026
Viewed by 362
Abstract
Pulsator tests are used to characterize the bending fatigue strength of the tooth root. In these tests, the tooth root is loaded not by meshing with another gear but by applying a pulsating load to the tooth flank via a testing machine. This [...] Read more.
Pulsator tests are used to characterize the bending fatigue strength of the tooth root. In these tests, the tooth root is loaded not by meshing with another gear but by applying a pulsating load to the tooth flank via a testing machine. This leads to a different S-N curve with respect to the ones obtained through meshing gear tests. This study aims to investigate the impact of cracks in the tooth root on the results of pulsator and meshing tests. Here, we address the issue of load sharing modification during meshing due to the presence of a crack, and its influence on crack propagation. This approach is applied to a real-life example: estimating the finite life of meshing gears based on pulsator tests. This study aims to present an initial procedure for obtaining S-N curves for meshing gears based on those obtained from pulsator tests. The S-N curves obtained from the pulsator test are compensated for by adding the difference in the propagation speed between the two tests calculated by applying the Paris law with parameters extracted from FE simulation; the time spent in propagation is almost doubled in the meshing conditions. Full article
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20 pages, 10034 KB  
Article
A Two-Wheel-Centric Reconfigurable Mobility Platform Enabled by Compact Steering–Drive–Suspension Modules: Balance, Driving, and Cooperative Transport
by Junghyun Choi
Machines 2026, 14(6), 704; https://doi.org/10.3390/machines14060704 - 19 Jun 2026
Viewed by 264
Abstract
Modern logistics and manufacturing environments simultaneously demand mobility platforms that are compact enough to navigate narrow aisles and powerful enough to transport oversized or heavy components. We previously developed a compact Steering–Drive–Suspension (SDS) module that integrates steering, in-wheel drive, and suspension within a [...] Read more.
Modern logistics and manufacturing environments simultaneously demand mobility platforms that are compact enough to navigate narrow aisles and powerful enough to transport oversized or heavy components. We previously developed a compact Steering–Drive–Suspension (SDS) module that integrates steering, in-wheel drive, and suspension within a single wheel envelope, achieving ±90∘ wide-angle steering with a single actuator. The present paper extends that hardware-centric work by treating the two-wheel (2WD) configuration assembled from two SDS modules as the unit module of the platform, building a four-wheel (4WD) operation by coupling two such 2WD units, and developing a unified balance and impedance-based control scheme. We derive a cart–pole inverted-pendulum model for the 2WD configuration and a planar 2-DOF bicycle model for the coupled and cooperative configurations, with full controllability proof and quantitative LQR robustness margins. Three Python 3.12 based scenarios validate the framework: (i) a 2WD inverted-pendulum tracking task, (ii) a forward and lateral relocation maneuver compared across SDS Crab, Ackermann, and four-wheel-steering modes, and (iii) cooperative transport of a 100kg steel plate by two impedance-coupled 2WD units. Across all scenarios the proposed controllers achieve sub-centimetre tracking gap, pitch deviation within ±2∘, and well-damped cooperative behavior without payload sloshing. The results substantiate the central design claim that the SDS module’s compactness enables a single hardware platform to act simultaneously as an autonomous small-payload mover, a building block of a 4WD platform, and a cooperative agent for oversized loads. Full article
(This article belongs to the Special Issue Advances in Automotive Mechatronics)
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29 pages, 22931 KB  
Article
Side-Impact Crashworthiness of Low-Emission Electric Bus with Battery-Integrated Inter-Window Pillars Under UNECE R95 Conditions
by Kostiantyn Holenko, Oleksandr Dykha, Anna Piętocha, Ivan Kernytskyy, Orest Horbay, Wojciech Górski and Eugeniusz Koda
Machines 2026, 14(6), 703; https://doi.org/10.3390/machines14060703 - 19 Jun 2026
Viewed by 304
Abstract
This study investigates the side-impact crashworthiness of a low-floor electric bus with traction batteries integrated into the inter-window pillars of the body structure. A finite-element model of the bus body was developed in Ansys and used to evaluate six impact scenarios involving conventional [...] Read more.
This study investigates the side-impact crashworthiness of a low-floor electric bus with traction batteries integrated into the inter-window pillars of the body structure. A finite-element model of the bus body was developed in Ansys and used to evaluate six impact scenarios involving conventional diesel and battery-integrated configurations. The analysis included evaluation of von Mises stresses, structural safety margins, deformation fields, strain energy, and transient nodal velocity response. The battery-integrated configuration demonstrated improvements in key crashworthiness indicators across the investigated impact scenarios, with both the average maximum deformation and the averaged equivalent stress reduced by approximately one quarter compared with the conventional configuration. The stress state of the inter-window pillars remained below the local structural failure levels observed in the conventional configuration, with the maximum pillar stress criterion reduced by more than half. Simultaneously, lower transient nodal velocities indicated reduced transmission of impact momentum toward the occupant compartment and more efficient redistribution of impact energy through the body structure. The results demonstrate the feasibility of using battery-integrated inter-window pillars as multifunctional structural members that simultaneously serve as energy storage and enhance the side-impact crashworthiness of low-floor electric buses. Full article
(This article belongs to the Special Issue Trends and Challenges in the Development and Operation of Modern Vehicles)
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39 pages, 9781 KB  
Article
Real-Time Big Data Pipelines for Industrial Robot Digital Twins: An OMPL Benchmarking Framework
by Metin Yılmaz, Cem Suha Yılmaz, Serhat Kahraman and Uğur Yayan
Machines 2026, 14(6), 702; https://doi.org/10.3390/machines14060702 - 18 Jun 2026
Viewed by 329
Abstract
The seamless integration of real-time operational technology (OT) with big data architectures remains a critical bottleneck in developing robust robotic Digital Twins. Furthermore, evaluating stochastic motion planners strictly within pristine simulations obscures vital real-world challenges such as sensor noise, communication latency, and mechanical [...] Read more.
The seamless integration of real-time operational technology (OT) with big data architectures remains a critical bottleneck in developing robust robotic Digital Twins. Furthermore, evaluating stochastic motion planners strictly within pristine simulations obscures vital real-world challenges such as sensor noise, communication latency, and mechanical stress. This study presents a high-throughput, real-time Hardware-in-the-Loop (HIL) pipeline integrating ROS 2, Apache Kafka, and Functional Mock-up Units (FMUs). Using a UR10e manipulator in a constrained industrial environment, we conducted extensive physical benchmarking of 11 Open Motion Planning Library (OMPL) algorithms across 10 repetitions, generating a comprehensive dataset of 785,192 samples. The proposed IT/OT architecture achieved deterministic millisecond-level synchronization, bounding end-to-end communication latency between 0.09 and 15.51 ms. Physical executions revealed a macroscopic “topological divergence” between simulation and reality, with spatial deviations peaking at 457.65 mm due to real-world point-cloud noise. While algorithms like EST and KPIECE demonstrated optimal geometric efficiency (e.g., a mean path length of 14.57 m) and hardware-friendly dynamics, traditional planners like RRT generated severe inertial spikes of up to 100 N, demonstrating substantial unsuitability for continuous industrial deployment. The primary contribution is a methodologically novel, rigorously validated big data pipeline and the release of an open-source, 50 Hz multimodal dataset (spatial, temporal, and dynamic forces), bridging the sim-to-real gap and providing a foundational benchmark for future data-driven robotic applications. Full article
(This article belongs to the Special Issue Robot Operating System: Integrated Robotic Planning and Control)
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24 pages, 15691 KB  
Article
A Joint Fault Diagnosis and Severity Prediction Framework for Rolling Bearings Using PPCA-EMD and 1DCNN-BiGRU
by Wangshen Hao, Chunhui Zhu, Dongliang Zou, Chenyang Li, Shenglin Song and Shilong Zhang
Machines 2026, 14(6), 701; https://doi.org/10.3390/machines14060701 - 18 Jun 2026
Viewed by 293
Abstract
Rolling bearing fault diagnosis remains challenging due to environmental noise, insufficient information sharing between diagnosis and prediction tasks, and poor model generalization ability. To address these issues, this paper proposes a fault diagnosis and severity prediction method integrating probabilistic principal component analysis (PPCA) [...] Read more.
Rolling bearing fault diagnosis remains challenging due to environmental noise, insufficient information sharing between diagnosis and prediction tasks, and poor model generalization ability. To address these issues, this paper proposes a fault diagnosis and severity prediction method integrating probabilistic principal component analysis (PPCA) and empirical mode decomposition (EMD) with a one-dimensional convolutional neural network (1DCNN) and bidirectional gated recurrent unit (BiGRU). The proposed model consists of two parallel branches for fault diagnosis and fault severity prediction. A self-attention mechanism is integrated into both branches to enhance feature extraction via adaptive feature weighting. In addition, parameter sharing and weighted loss functions are adopted to improve the training efficiency and collaborative learning between the two tasks. PPCA and EMD are employed for signal denoising and reconstruction while preserving fault-related features. Experiments on public datasets and industrial production-line data show that the proposed method improves the fault classification accuracy from 92.43% to 99.71% under different load conditions, while achieving 98.99% accuracy in fault severity prediction. Noise interference tests further demonstrate the effectiveness of the model. A production-line case study further illustrates the feasibility of applying the proposed method to real monitoring signals. These results confirm the effectiveness and practical potential of the proposed method for rolling bearing fault diagnosis and health assessment. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Perception and Diagnosis of Mechanical Equipment)
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18 pages, 4355 KB  
Article
An Unknown Payload Mass Prediction Method Using Fuzzy Logic Compensation and Pre-Acquired Volume Information
by Xun Chen, Haoyi Wu, Chunlin Pang, Xinze Hu, Xin Chen and Guohuai Lin
Machines 2026, 14(6), 700; https://doi.org/10.3390/machines14060700 - 18 Jun 2026
Viewed by 314
Abstract
In this article, a fuzzy payload compensation algorithm is proposed. In the context of simulating a machine vision model reconstruction, the target object is regarded as a cylinder to obtain the corresponding geometric size data. The first fuzzy mass prediction system is then [...] Read more.
In this article, a fuzzy payload compensation algorithm is proposed. In the context of simulating a machine vision model reconstruction, the target object is regarded as a cylinder to obtain the corresponding geometric size data. The first fuzzy mass prediction system is then used to predict the mass of the target object. During operation, real-time processing and calculation of the robotic arm’s joint motor current data are performed. Based on the mathematical relationship between the identified basic parameter set from the dynamic parameters and the end-effector payload, the second fuzzy compensation system was used to calculate the root mean square error (RMSE) of the predicted versus collected current data of the 6-th joint motor, thereby predicting and compensating for the payload mass. The final prediction is generated upon completion of the operation. The overall experiment is conducted on the HSR-CR607 robot. The experimental results indicated that the proposed prediction algorithm consistently operates within the acceptable error range (15%) in most test cases. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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17 pages, 6241 KB  
Article
Performance Optimization of Nuclear Reheat Valve Considering Coned-Disc Spring with Simulation and Experimental Methods
by Yongjie Wen, Yanxiong Liu, Zhicheng Xu, Yinhui Che, Cheng Shu and Kai Hu
Machines 2026, 14(6), 699; https://doi.org/10.3390/machines14060699 - 18 Jun 2026
Viewed by 282
Abstract
The dynamic reliability of steam-turbine governing systems is essential for the safe operation of nuclear power units. As a key regulating and protection component, the reheat valve must complete rapid closure under abnormal operating conditions. This study addresses the closing timeout problem observed [...] Read more.
The dynamic reliability of steam-turbine governing systems is essential for the safe operation of nuclear power units. As a key regulating and protection component, the reheat valve must complete rapid closure under abnormal operating conditions. This study addresses the closing timeout problem observed in a nuclear reheat-valve oil-motor actuator after domestic substitution, with particular attention to sluggish motion and discontinuous closing at small openings. A coupled hydraulic–mechanical model was then established by integrating the coned-disc spring assembly, hydraulic circuit, cartridge valve, gear–rack transmission, and load resistance based on the mathematical model. The model was used to identify the dominant parameters controlling the fast-closing process, and the optimization strategy was subsequently verified by experiments on an actual actuator platform. The results show that coned-disc spring degradation is a critical source of closing timeout risk. When the equivalent elastic modulus decreases to approximately 195 GPa, the fast-closing time approaches the critical limit of 0.8 s, while further degradation results in evident timeout. The C0 throttling orifice has the strongest influence on the effective closing time by governing the pressure-relief capacity of the working chamber. A coordinated correction strategy, involving coned-disc spring force compensation and throttling parameter adjustment, restores the closing margin, shortens the fast-closing time to 0.78 s, and improves closing smoothness. This work provides the practical guidance for design verification, field commissioning, and domestic improvement of nuclear reheat-valve oil-motor actuator systems. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Predictive Maintenance for Mechatronic-Hydraulic Systems)
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16 pages, 8200 KB  
Article
A Bearing Fault Diagnosis Method Integrating the SWT and MCNN−RIME−KELM Hybrid Model
by Liping Wang, Xing Liu, Xiaoke Su and Dongyao Zou
Machines 2026, 14(6), 698; https://doi.org/10.3390/machines14060698 - 18 Jun 2026
Viewed by 314
Abstract
To address the issues of severe noise interference, limited classification capability of linear classifiers, and difficulty in adaptively optimizing classifier parameters in rolling bearing fault diagnosis, this paper proposes a hybrid diagnostic model integrating the multi−scale convolutional neural network and rime ice optimization [...] Read more.
To address the issues of severe noise interference, limited classification capability of linear classifiers, and difficulty in adaptively optimizing classifier parameters in rolling bearing fault diagnosis, this paper proposes a hybrid diagnostic model integrating the multi−scale convolutional neural network and rime ice optimization algorithm optimized kernel extreme learning machine. The method first employs the synchrosqueezed wavelet transform to convert raw vibration signals into high−resolution time−frequency images, effectively enhancing the visualization of fault impact features. Then, the multi−scale convolutional neural network is used to extract preliminary features from the time−frequency images, and the kernel extreme learning machine is introduced to replace the Softmax linear classifier in traditional convolutional neural networks, thereby constructing a nonlinear decision boundary to more effectively separate complex fault patterns. Finally, the rime algorithm is introduced to optimize the regularization coefficient and kernel parameters of the kernel extreme learning machine, enabling the kernel extreme learning machine to perform fault classification with an optimal nonlinear decision boundary. Experimental results on the bearing datasets from Huazhong University of Science and Technology and Case Western Reserve University show that the proposed method achieves classification accuracies of 99.75% and 99.83%, respectively, outperforming several comparison models. Furthermore, noise robustness experiments demonstrate that the proposed model maintains an accuracy of approximately 90% under low signal−to−noise ratio (SNR) conditions, outperforming all comparison models and demonstrating high classification accuracy under strong noise. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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28 pages, 4858 KB  
Article
Hopf Bifurcation Characteristics of a Magnetic Liquid Double-Suspension Bearing Rotor System
by Xinwei Wang, Xv Zhang, Hanwen Zhang and Jianhua Zhao
Machines 2026, 14(6), 697; https://doi.org/10.3390/machines14060697 - 17 Jun 2026
Viewed by 278
Abstract
To reveal the nonlinear instability mechanism by which the three-degree-of-freedom rotor system of a magnetic-liquid double suspension bearing transforms from stable suspension to periodic vibration, a nonlinear dynamic model considering electromagnetic suspension force, hydrostatic supporting force, rotor unbalance force, and liquid film resistance [...] Read more.
To reveal the nonlinear instability mechanism by which the three-degree-of-freedom rotor system of a magnetic-liquid double suspension bearing transforms from stable suspension to periodic vibration, a nonlinear dynamic model considering electromagnetic suspension force, hydrostatic supporting force, rotor unbalance force, and liquid film resistance is established. The equilibrium point of the system is linearized, and the Hopf bifurcation boundary is determined using the Routh–Hurwitz criterion. Numerical simulations are then carried out to investigate the effects of the initial current i0, supply flow rate q0, and different initial disturbances on the displacement time histories, phase trajectories, and spatial phase trajectories of the rotor. The results show that, under the given system parameters, the Hopf bifurcation boundary is 0.61 A for the initial current and 9.62 × 10−5 m3/s for the supply flow rate. Current variation mainly affects electromagnetic stiffness and nonlinear electromagnetic force, whereas flow rate variation primarily changes the hydrostatic load capacity and oil film damping characteristics. Under different initial disturbances, the system may exhibit amplitude attenuation, recovery to stable suspension, or finite amplitude periodic vibration. Experimental results show good agreement with numerical simulations in terms of frequency spectra, displacement time histories, and phase trajectories, thereby verifying the effectiveness of the proposed three-degree-of-freedom dynamic model and Hopf bifurcation analysis method. The results can provide theoretical guidance for parameter matching, stability evaluation, and self-excited vibration suppression of magnetic-liquid double suspension bearings. Full article
(This article belongs to the Section Electrical Machines and Drives)
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26 pages, 2413 KB  
Article
UAV-Assisted Preview-Augmented DSMC with Control Barrier Functions for Safe and Robust Trajectory Tracking of AGVs
by Umar Farid, Muhammad Usman Jamil and Zahid Ullah
Machines 2026, 14(6), 696; https://doi.org/10.3390/machines14060696 - 17 Jun 2026
Viewed by 876
Abstract
Autonomous navigation of a vehicle in an environment where there are obstacles is difficult due to low onboard sensing technology, high measuring noise, and external interference, which collectively result in poor tracking performance of the vehicle’s trajectory and compromise safety. In this paper, [...] Read more.
Autonomous navigation of a vehicle in an environment where there are obstacles is difficult due to low onboard sensing technology, high measuring noise, and external interference, which collectively result in poor tracking performance of the vehicle’s trajectory and compromise safety. In this paper, a UAV-assisted Distributed Sliding Mode Control (DSMC) is proposed to robustly and safely implement path tracking for autonomous ground vehicles (AGVs). The proposed system utilizes an aero-sensor layer for enhanced perception, such as obstacle sensing, reference path preview, and look-ahead trajectory information, and it shares this information with the vehicle via wireless communication. The fundamental scheme, called DSMC, is based on a conventional Sliding Mode Control (SMC) technique and uses UAV preview-based feedback. This allows anticipation of control actions to enhance tracking performance and achieve more timely, smoother obstacle avoidance than baseline SMC. The proposed method is designed to overcome the limitations of traditional SMC strategies, such as chattering and poor responsiveness. The proposed method features continuous nonlinear approximation and damping mechanisms to reduce chattering and improve response characteristics, thereby enhancing stability and reducing oscillations. Strict safety enforcement through constraint is always achieved by keeping the vehicle and obstacles separated by a minimum distance only; that is, a minimum distance is always guaranteed: a Constraint Barrier Function (CBF)-based constraint is used. By combining UAV-assisted perception with DSMC and CBF the system can guarantee its formal safety in the presence of disturbances and sensing uncertainties while maintaining accurate trajectory tracking. Based on our simulation results, the proposed UAV-assisted DSMC method is shown to be significantly superior to conventional SMC and Model Predictive Controller (MPC) in terms of tracking accuracy, control smoothness, and adherence to the safety margin. Our simulation results demonstrate that the proposed method significantly outperforms conventional SMC and MPC control. Specifically, it achieves a 22.9% reduction in RMSE (0.135 m vs. 0.175 m) and 63% lower mean control effort, and it strictly maintains the minimum safety distance under both static and dynamic obstacles. The algorithm runs in real-time with an average execution time of 1.85 ms (>200 Hz), making it highly suitable for embedded deployment. These results highlight the effectiveness of combining UAV-assisted preview, adaptive robust control, and formal safety constraints for reliable autonomous navigation in complex environments. Full article
(This article belongs to the Special Issue Advances in Automotive Mechatronics)
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36 pages, 10549 KB  
Article
A Multi-Class Predictive Maintenance Framework for Jet Engines Using the C-MAPSS Dataset
by Bowen Dong, Xinyu Zhang, Lingmin Hou, Chaoya Yan, Yifan Feng, Weiyan Zhu and Lixing Lin
Machines 2026, 14(6), 695; https://doi.org/10.3390/machines14060695 - 17 Jun 2026
Viewed by 340
Abstract
Aero-engine predictive maintenance is challenged by heterogeneous operating conditions, complex degradation patterns, and the need for interpretable maintenance alerts rather than solely numerical life estimates. This study investigates a condition-aware data-driven framework for jet engine health assessment using the NASA C-MAPSS dataset, which [...] Read more.
Aero-engine predictive maintenance is challenged by heterogeneous operating conditions, complex degradation patterns, and the need for interpretable maintenance alerts rather than solely numerical life estimates. This study investigates a condition-aware data-driven framework for jet engine health assessment using the NASA C-MAPSS dataset, which contains four benchmark subsets (FD001–FD004) with different operating conditions and fault modes. Instead of formulating the task as conventional remaining useful life regression, this study reformulates degradation assessment as a three-class health state classification problem, including Normal, Warning, and Fault. A unified preprocessing pipeline is developed, incorporating condition-wise normalization, first-order differential feature construction, and per-unit sliding window segmentation to reduce operating-condition bias, capture degradation dynamics, and prevent data leakage. Five representative models are evaluated under the same framework, including XGBoost, LightGBM, Random Forest, a context-aware multi-scale temporal attention convolutional neural network, and a bidirectional long short-term memory network. The results show that the proposed framework achieves consistently high classification accuracy across all four subsets, with the best results of 0.9841 on FD001, 0.9764 on FD002, 0.9891 on FD003, and 0.9832 on FD004. In addition, Bi-LSTM outperforms MSTA-CNN on all subsets, for example improving accuracy from 0.9614 to 0.9747 on FD002 and from 0.9773 to 0.9806 on FD004, which is consistent with the importance of long-term temporal dependency modeling for this task. These findings suggest that the proposed framework provides an effective and maintenance-decision-aligned solution for C-MAPSS-based health monitoring, where the three-class alert output offers clearer operational meaning than a single numerical life estimate. Full article
(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)
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33 pages, 8848 KB  
Article
A Fault Identification Method for EHA Multivariate Time Series Based on Multi-View Heterogeneous Ensemble Learning
by Guozhu Zhi, Kelin Zhong, Zhen Jia, Weijun Yan, Zhihao Gao, Baodong Wang, Qingqing Dang and Zhenbao Liu
Machines 2026, 14(6), 694; https://doi.org/10.3390/machines14060694 - 17 Jun 2026
Viewed by 294
Abstract
Accurate fault classification of electro-hydrostatic actuators (EHAs) remains challenging because multivariate fault signals contain local transient variations, inter-variable coupling, and dynamic temporal dependencies that are difficult to capture simultaneously using a single model. To address this problem, this paper proposes a multi-view temporal [...] Read more.
Accurate fault classification of electro-hydrostatic actuators (EHAs) remains challenging because multivariate fault signals contain local transient variations, inter-variable coupling, and dynamic temporal dependencies that are difficult to capture simultaneously using a single model. To address this problem, this paper proposes a multi-view temporal feature collaborative heterogeneous ensemble learning model (MTF-HEM) for EHA multivariate time series fault classification. MTF-HEM integrates a representative subsequence-guided time series forest (RSG-TSF), XGBoost, and a lightweight LSTM to extract local morphological, global statistical, and temporal dependency features, respectively. The outputs of these heterogeneous base learners are fused using a bootstrap-driven out-of-bag probability binning stacking (BOPB-stacking) strategy. The proposed method was evaluated on an AMESim-based simulated EHA plunger pump fault dataset containing one normal condition and six fault conditions. Under the present simulation setting, MTF-HEM achieved an accuracy of 99.52% and outperformed the tested deep time series classification models, ensemble models, and individual base learners. These results suggest that multi-view heterogeneous feature fusion can improve the classification of simulated EHA fault time series and provide a methodological reference for intelligent actuator fault diagnosis. However, the current validation is based on data generated from a single AMESim simulation model, and further evaluation on real EHA systems is needed to assess the practical applicability and generalizability of the proposed approach. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault Tolerant Control in Mechanical System)
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26 pages, 1733 KB  
Article
Generalized Inverter Fault Detection Using Normalized Current Features and a Lightweight BiLSTM Network
by Mohammad Zamani Khaneghah, Mohamad Alzayed and Hicham Chaoui
Machines 2026, 14(6), 693; https://doi.org/10.3390/machines14060693 - 17 Jun 2026
Viewed by 317
Abstract
Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a [...] Read more.
Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on operating conditions, enabling the model to generalize across different load levels and motor power ratings without retraining. A lightweight BiLSTM architecture is employed, reducing computational complexity while maintaining high diagnostic performance. The proposed method is validated under various operating conditions, including different speeds, load variations, motor power ratings, and noisy conditions. The results demonstrate an overall classification accuracy of 99.65%, with reliable fault detection achieved within less than half of a fundamental cycle. The proposed approach provides an efficient, robust, and scalable solution for inverter fault detection and diagnosis, offering strong potential for practical deployment in modern motor drive systems. Full article
(This article belongs to the Special Issue Data-Driven RUL Prediction: Innovations in Generalization, Uncertainty, and Efficiency for Industrial PHM)
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19 pages, 23513 KB  
Article
Multi-Objective Crashworthiness Optimization of Variable-Thickness Expansion Tubes Using Machine Learning and Decision-Making
by Dezhuang Yu, Haitao Dong, Zhanyu Liu, Weiyuan Guan and Jijian Lu
Machines 2026, 14(6), 692; https://doi.org/10.3390/machines14060692 - 16 Jun 2026
Viewed by 305
Abstract
While traditional expansion tubes exhibit excellent energy absorption, their uniform wall thickness limits lightweighting and performance optimization. Graded thickness designs can reduce the initial peak crushing force (IPCF) and enhance material efficiency. This paper proposes a variable-thickness expansion tube integrating high [...] Read more.
While traditional expansion tubes exhibit excellent energy absorption, their uniform wall thickness limits lightweighting and performance optimization. Graded thickness designs can reduce the initial peak crushing force (IPCF) and enhance material efficiency. This paper proposes a variable-thickness expansion tube integrating high energy absorption with tailored mechanical response. Material tensile tests were conducted to determine the constitutive relationship, and axial compression experiments on expansion tubes were performed. Numerical simulations were validated against experimental results, establishing an accurate finite element model. The influence of design parameters on crashworthiness indicators was analyzed via orthogonal experiments. A fully connected neural network with a feature importance layer was then constructed to efficiently replace computationally expensive simulations. Key performance indicators—including IPCF, total energy absorption (EA), and structural mass (m)—were synergistically optimized using a multi-objective genetic algorithm. Finally, the entropy weight–gray relation–TOPSIS method was employed to select the most satisfactory solution from the Pareto front. The relative discrepancies between the selected solution and finite element simulations are 3.65% for EA, 0.23% for mass, and 4.37% for IPCF, confirming the framework’s reliability. This study establishes a systematic design approach combining machine learning, multi-objective optimization, and multi-criteria decision-making for high-performance energy-absorbing structures. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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23 pages, 9294 KB  
Article
Prediction of Dynamic Characteristics and Control Parameter Optimization for Precision Motion Stages by Integrating Generalized Receptance Coupling Substructure Analysis and Machine Learning
by Fengguo Li, Peng Yao, Yao Hou, Xinyu Mao, Zhonglei Zhang, Hongyi Sun, Jiarong Bai, Jubin Zhang, Tonghui Hu, Wei Wu, Jiaofeng Ma, Yang Yu and Wenxiu Yu
Machines 2026, 14(6), 691; https://doi.org/10.3390/machines14060691 - 16 Jun 2026
Viewed by 274
Abstract
To address the complex dynamic behavior of four-axis precision motion platforms under high-speed and high-acceleration conditions, as well as the difficulty of traditional modeling methods in balancing accuracy and efficiency, this paper proposes a data/model-driven dynamic modeling and analysis method that integrates generalized [...] Read more.
To address the complex dynamic behavior of four-axis precision motion platforms under high-speed and high-acceleration conditions, as well as the difficulty of traditional modeling methods in balancing accuracy and efficiency, this paper proposes a data/model-driven dynamic modeling and analysis method that integrates generalized receptance coupling substructure analysis (GRCSA) with artificial intelligence (AI) algorithms. Based on the GRCSA theory, the initial analytical framework of the dynamic model of the precision motion platform is established, and the frequency response functions (FRFs) of the substructure and interface are preliminarily obtained. On this basis, the nonlinear prediction model of the dynamic parameters of the interface driving direction is established by using the AI algorithm, enabling fast and accurate prediction of the dynamic characteristics of the interface under different servo control parameters in the guide rail driving direction. Finally, based on the data/model-driven dynamic modeling and analysis method, the interface control parameters are optimized. The interface and substructure parameters are modified to reduce the prediction error of the FRFs from 3.50% to 2.47%. This method can achieve the prediction error of the dynamic characteristics of the interface under different control parameters of about 2.5%. Full article
(This article belongs to the Section Automation and Control Systems)
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25 pages, 4707 KB  
Article
Multi-Temperature Zone Active Thermal Control Using Feedforward Decoupling Integrated MPC–PID for Machine Tool
by Baoying Peng, Chaoran Liang, Kaichun Bo, Ruiqian Zhang and Xingyu Zhao
Machines 2026, 14(6), 690; https://doi.org/10.3390/machines14060690 - 15 Jun 2026
Viewed by 290
Abstract
Existing machine tool thermal error mitigation relying on passive structural optimization and conventional feedforward PID decoupling poorly addresses strong multi-temperature-field coupling, large time delays, and nonlinear thermal characteristics in large precision horizontal machining centers. These methods lack predictive optimization, fail to suppress the [...] Read more.
Existing machine tool thermal error mitigation relying on passive structural optimization and conventional feedforward PID decoupling poorly addresses strong multi-temperature-field coupling, large time delays, and nonlinear thermal characteristics in large precision horizontal machining centers. These methods lack predictive optimization, fail to suppress the long-term temperature drift of key structural components, and cannot realize active thermal intervention, leaving a clear research gap. This paper develops a three-layer closed-loop active thermal control framework with temperature sensing, numerical decoupling, and executive regulation. S-shaped hollow aluminum temperature control plates are optimally arranged on the bed, column, and beam, and a multi-temperature zone coupling transfer function model is established. A hybrid control strategy integrating feedforward decoupling, MPC prediction, and PID steady-state compensation is proposed; MPC is introduced to handle multivariable coupling, time lag, and actuator constraints beyond the capability of traditional PID. Comparative experiments show that the MPC-based scheme reduces key point temperature variation rates by 31.47%, 14.56%, 16.06% and 44.86%. This study focuses on temperature stabilization (rather than the direct measurement of the spindle drift or geometric deformation). The proposed method provides an effective active temperature balance solution for large precision machine tools. Full article
(This article belongs to the Section Automation and Control Systems)
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29 pages, 6187 KB  
Article
Relation Knowledge-Guided Federated Model Compression for Rare-Fault Preservation in Motor Fault Diagnosis
by Genbao Zhao and Juan Zhang
Machines 2026, 14(6), 689; https://doi.org/10.3390/machines14060689 - 15 Jun 2026
Viewed by 262
Abstract
To address global knowledge bias, weak rare-fault recognition, and high edge-deployment costs caused by heterogeneous sample sizes, data quality, fault categories, and monitoring modalities among multiple clients, this paper proposes a rare-fault-preserving federated dynamic model slimming method based on relational knowledge. The core [...] Read more.
To address global knowledge bias, weak rare-fault recognition, and high edge-deployment costs caused by heterogeneous sample sizes, data quality, fault categories, and monitoring modalities among multiple clients, this paper proposes a rare-fault-preserving federated dynamic model slimming method based on relational knowledge. The core idea is to formulate lightweight federated diagnosis as a joint optimization problem of rare-fault knowledge preservation and redundant knowledge suppression. At each local client, output-discriminative knowledge, class-prototype relations, and input-sensitive relations are extracted to describe diagnostic knowledge from the decision, structure, and weak-response levels. At the federated server, a rare-fault-aware weighting mechanism adjusts the contribution of local knowledge according to sample scarcity, output reliability, and distribution dispersion and then fuses multi-granularity relational knowledge to optimize the global teacher model. A relation-constrained gated slimming strategy is further designed for the student model, enabling the lightweight model to retain critical diagnostic channels while suppressing repetitive and low-contribution information. Experiments on the CWRU bearing dataset and the HUST multimodal motor dataset show that the proposed method achieves higher diagnostic accuracy, rare-fault recall, and deployment efficiency under composite imbalance, cross-condition generalization, and modality-missing deployment scenarios. These results demonstrate the effectiveness of the proposed method for raw-data-free and privacy-aware multi-client motor fault diagnosis. Full article
(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)
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41 pages, 61506 KB  
Article
Research on Autonomous Navigation System of Drilling Robots for Coal Mine Gas Outburst Prevention
by Shaoze You, Menggang Li, Chaoquan Tang and Jun Wang
Machines 2026, 14(6), 688; https://doi.org/10.3390/machines14060688 - 14 Jun 2026
Viewed by 256
Abstract
Underground gas control is a critical link in coal mine safety production, and drilling robots serve as the core equipment for gas extraction drilling operations. However, the autonomous locomotion technology of coal mine drilling robots has long been constrained by the unstructured underground [...] Read more.
Underground gas control is a critical link in coal mine safety production, and drilling robots serve as the core equipment for gas extraction drilling operations. However, the autonomous locomotion technology of coal mine drilling robots has long been constrained by the unstructured underground environment and the limitations of existing navigation schemes, which restrict their intelligent application. To address this bottleneck, this paper conducts systematic research on key autonomous navigation technologies for coal mine drilling robots operating in narrow underground working faces, focusing on their practical operational requirements. First, a hardware scheme complying with coal mine safety standards is selected, the hardware structure and sensor layout are optimized via digital modeling, and the software interface and data interface format of the navigation system are designed. Second, an innovative 3D point cloud-based offline obstacle avoidance algorithm is proposed, which integrates a terrain analysis module, a local path planning method with maximum arrival probability, a Bézier curve-based trajectory library generation strategy, and a trajectory index construction method. Finally, simulation experiments, ground-simulated roadway field tests, and underground coal mine field experiments are performed to validate the proposed system. Experimental results demonstrate that the constructed autonomous navigation system enables smooth and safe autonomous locomotion and fixed-point parking of drilling robots, with an average parking error lower than 0.17 m, and can effectively avoid obstacles in complex environments. This research provides crucial technical support for the intelligent advancement of coal mine drilling robots. Full article
(This article belongs to the Topic Advances in Autonomous Vehicles, Automation, and Robotics)
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25 pages, 6975 KB  
Article
SIFT-NRBO-VMD-Transformer: A Vision-Based Data-Driven Interface Morphology Prediction Framework for Intelligent Wear Diagnosis of Wet Friction Components
by Yue Zhao, Yingli Li, Fangwei Luo, Xi Chen, Hongqiao Yan and Molin Su
Machines 2026, 14(6), 687; https://doi.org/10.3390/machines14060687 - 14 Jun 2026
Viewed by 252
Abstract
Wet friction components are critical to power transmission in petroleum drilling machinery, where their reliability directly affects system stability. Surface defects, such as scratches and plowing grooves, can significantly degrade transmission performance, highlighting the importance of interface morphology prediction for intelligent wear diagnosis. [...] Read more.
Wet friction components are critical to power transmission in petroleum drilling machinery, where their reliability directly affects system stability. Surface defects, such as scratches and plowing grooves, can significantly degrade transmission performance, highlighting the importance of interface morphology prediction for intelligent wear diagnosis. In this study, interface morphology data under different conditions are acquired using a UMT-Tribolab test platform and a white light interferometer. The Scale-Invariant Feature Transform (SIFT) algorithm is employed to achieve precise localization of microscopic regions before and after testing. Based on this, an NRBO-VMD-Transformer model is developed to predict the interface morphology of wet friction components under varying conditions. The results demonstrate that SIFT enables accurate localization of microscopic regions, while the proposed model achieves high-precision prediction of interface morphology evolution. These findings provide a reliable basis for interface morphology prediction and wear evolution analysis of wet friction components. Full article
(This article belongs to the Special Issue Intelligent Predictive Maintenance and Machine Condition Monitoring)
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18 pages, 28508 KB  
Article
An End-Effector Grasping Strategy for Dual-Arm Robots During Construction Board Installation
by Zhengjiu Ma, Yuxin Liu, Yongbin Li, Zhi Niu, Zhaoqing Kang, Zedan Li, Tong Wang and Tiejun Li
Machines 2026, 14(6), 686; https://doi.org/10.3390/machines14060686 - 12 Jun 2026
Viewed by 237
Abstract
The dual-arm cooperative operation mode can effectively address the problems of insufficient load capacity and limited motion flexibility of traditional single-arm robots during the installation of construction boards. However, the selection of the end-effector grasping position of dual-arm robots will significantly affect their [...] Read more.
The dual-arm cooperative operation mode can effectively address the problems of insufficient load capacity and limited motion flexibility of traditional single-arm robots during the installation of construction boards. However, the selection of the end-effector grasping position of dual-arm robots will significantly affect their motion performance during handling operations. To address this issue, this study proposes an end-effector grasping strategy for sheet installation in the dual-arm cooperative operation mode of a dual-arm robot, which determines the optimal grasping position to ensure the robot’s good operational performance. We developed a dual-arm robot prototype for board installation and established a kinematic model of the robot’s manipulators. Based on the dexterity index’s service sphere, we obtained the dexterity envelope surfaces of the robot end-effector at different grasping distances and analyzed the relationship between grasping distance and dexterity. The mechanical model of the robot was established, and simulations were performed for each joint. The effects of different grasping points on the torque, stiffness, and stability at the robot’s key points were investigated, and the end-effector grasping range of the robot with optimal mechanical performance was analyzed. Finally, the proposed robot grasping strategy was verified on the robot prototype. The results demonstrate that the strategy is feasible and effective, helping to improve the robot’s operational performance. Full article
(This article belongs to the Section Automation and Control Systems)
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15 pages, 4391 KB  
Article
Risk-Aware Edge-Assisted UAV Perception with Confidence and SLA Gating
by Nizamuddin Maitlo, Rafaqat Hussain Arain, Kaleem Arshid, Nooruddin Noonari and Ghulam Mustafa
Machines 2026, 14(6), 685; https://doi.org/10.3390/machines14060685 - 12 Jun 2026
Viewed by 455
Abstract
Autonomous unmanned aerial vehicles (UAVs) must decide when to trust onboard perception, when to request edge support, and when to avoid acting under poor visual or communication conditions. This study develops a risk-aware edge-assisted UAV perception framework that combines calibrated visual confidence with [...] Read more.
Autonomous unmanned aerial vehicles (UAVs) must decide when to trust onboard perception, when to request edge support, and when to avoid acting under poor visual or communication conditions. This study develops a risk-aware edge-assisted UAV perception framework that combines calibrated visual confidence with next-window service-level agreement (SLA) feasibility. The local branch uses MobileNetV3-Small for fast onboard color recognition, while the edge branch uses ResNet-18 for stronger remote inference. Low-confidence samples are offloaded only when the SLA predictor estimates that the wireless link is feasible; otherwise, the system enters fallback, meaning that the current prediction is not treated as immediately actionable. The evaluation follows a hard cross-illumination split: indoor and fluorescent light samples are used for training and validation, and indoor night and sunlight samples are reserved for testing. Under this setting, the local model achieves 76.89% accuracy and 73.25% macro-F1, while the edge model achieves 81.26% accuracy and 77.58% macro-F1. The SLA predictor, trained on enhanced telemetry features while preserving the original target label, achieves 85.74% accuracy, 85.57% macro-F1, 0.9420 ROC-AUC, and 0.9585 PR-AUC on temporally held-out records. The joint policy achieves 93.23% coverage and 79.90% success over active decisions, using local inference for 82.76% of the samples, edge offloading for 10.47%, and fallback for 6.77%. These results indicate that the framework is best understood as a tunable risk management layer for UAV perception rather than a pure accuracy maximization classifier. It avoids blind offloading and reduces forced decisions when both visual confidence and communication feasibility are weak. Full article
(This article belongs to the Special Issue AI-Enabled and Secure Space–Air–Ground Systems: Integrated Sensing, Communication, and Autonomous Intelligence for UAV Networks)
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19 pages, 11623 KB  
Article
Study on a Fully Electrified Steering System and Its Control Strategies for Heavy-Duty Wheeled Platforms
by Shicheng Zheng, Tianxiang Qin, Jingkun Wei, Jiaming Cheng, Xiaming Yuan and Jihong Zhu
Machines 2026, 14(6), 684; https://doi.org/10.3390/machines14060684 - 12 Jun 2026
Viewed by 246
Abstract
To address the limitations of the centralized hydraulic steering system used in the first-generation heavy-duty wheeled platform developed by our team, this study proposes a fully electrified steering system based on a compact direct-drive electro-mechanical actuator (DEMA) architecture. Compared with the original hydraulic [...] Read more.
To address the limitations of the centralized hydraulic steering system used in the first-generation heavy-duty wheeled platform developed by our team, this study proposes a fully electrified steering system based on a compact direct-drive electro-mechanical actuator (DEMA) architecture. Compared with the original hydraulic system, the proposed solution reduces the steering-system weight from approximately 150 kg to 32 kg in the single-channel configuration and 40 kg in the dual-channel configuration, while significantly improving system integration and maintainability. For the single-channel DEMA steering system, a composite control strategy combining three-loop PID control with feedforward compensation is developed to improve dynamic response and position-tracking accuracy. AMESim simulation results under a steering resistance torque of 6000 ± 500 Nm show that the system achieves an overshoot below 2%, a steady-state error below 0.1°, and a tracking error below 0.4°. To reduce motor power and thermal-management requirements, a dual-channel DEMA steering architecture is further proposed. Considering inter-channel parameter differences, a primary–secondary synchronization control strategy is developed to suppress force-fighting behavior and improve motion consistency. Simulation results demonstrate that the proposed strategy effectively reduces synchronization errors and maintains highly consistent force output between channels while preserving excellent steering accuracy and tracking performance. The proposed fully electrified steering system and synchronization control strategy provide an effective solution for improving the dynamic performance, lightweight design, and reliability of heavy-duty wheeled platforms. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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16 pages, 3246 KB  
Article
Analytical Modeling and Analysis of High-Torque-Density Three-Segment Halbach Array PM Machine by Considering Leakage Flux
by Jinlin Huang, Qingfeng Sun and Chen Wang
Machines 2026, 14(6), 683; https://doi.org/10.3390/machines14060683 - 12 Jun 2026
Viewed by 280
Abstract
Conventional finite element method (FEM) has a complex model and a long optimization time for Halbach array PM machines. This paper proposes a hybrid analytical method that combines the subdomain method (SM) and the magnetic circuit method (MEC) for analyzing a high-torque-density, three-segment [...] Read more.
Conventional finite element method (FEM) has a complex model and a long optimization time for Halbach array PM machines. This paper proposes a hybrid analytical method that combines the subdomain method (SM) and the magnetic circuit method (MEC) for analyzing a high-torque-density, three-segment Halbach array rotor permanent magnet (PM) machine, accounting for Halbach array magnetization and end leakage flux. Firstly, to address the challenge posed by complex PM shapes in the Halbach array PM machine, a novel subdivision equivalence method is conducted. Then, the magnetic equivalent circuit (MEC) of the stator and rotor is established, and the axial leakage flux and nonlinearity of the iron core are taken into account. In addition, electromagnetic performance, such as air gap flux density, cogging torque, electromagnetic torque, and back electromotive force (back-EMF), is obtained based on the proposed hybrid analytical model. The analytical results are verified by using the finite element method (FEM), and the results show that the error is less than 2%. Finally, a 15 kW prototype PM machine with a Halbach array PM rotor is manufactured and tested, and the results validate the accuracy and efficiency of the analytical method. Full article
(This article belongs to the Special Issue Analysis of Electromagnetic, Thermal, and Vibro-Acoustic Behavior in Permanent Magnet (PM) Motors)
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29 pages, 2784 KB  
Article
Condition-Aware DANN-LSTM for Rolling-Bearing Fault Diagnosis and Remaining Useful Life Prediction Under Operating Condition Shifts
by Yangfeng Ji, Rongfei Xia and Miaojiao Peng
Machines 2026, 14(6), 682; https://doi.org/10.3390/machines14060682 - 11 Jun 2026
Viewed by 271
Abstract
Rolling element bearing monitoring under operating condition shifts remains difficult because fault signatures are transient, fault data are scarce, and degradation trends may depend on load and speed. This study evaluates a condition-aware DANN-LSTM framework for joint fault diagnosis and RUL prediction. A [...] Read more.
Rolling element bearing monitoring under operating condition shifts remains difficult because fault signatures are transient, fault data are scarce, and degradation trends may depend on load and speed. This study evaluates a condition-aware DANN-LSTM framework for joint fault diagnosis and RUL prediction. A one-dimensional CNN extracts vibration features, a gradient reversal branch aligns condition-related distributions for fault classification, and an LSTM models chronological degradation features without direct adversarial regularization. The model jointly optimizes classification, condition-discrimination, and RUL losses. Experiments on public bearing datasets show high class-wise identification rates, a validation accuracy of 0.989, and an RUL RMSE of 7.9. Controlled ablation indicates that moderate condition alignment improves transfer classification while preserving useful degradation ordering for RUL prediction. The framework offers a practical data-driven baseline for bearing condition monitoring under controlled condition shifts. Full article
(This article belongs to the Special Issue Recent Advances in Condition Monitoring and Fault Diagnosis of Rotating Machinery)
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