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Volume 13
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Machines, Volume 13, Issue 8 (August 2025) – 100 articles

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28 pages, 955 KiB  
Article
Trust-Based Modular Cyber–Physical–Human Robotic System for Collaborative Manufacturing: Modulating Communications
by S. M. Mizanoor Rahman
Machines 2025, 13(8), 731; https://doi.org/10.3390/machines13080731 (registering DOI) - 17 Aug 2025
Abstract
The objective was to propose a human–robot bidirectional trust-triggered cyber–physical–human (CPH) system framework for human–robot collaborative assembly in flexible manufacturing and investigate the impact of modulating communications in the CPH system on system performance and human–robot interactions (HRIs). As the research method, we [...] Read more.
The objective was to propose a human–robot bidirectional trust-triggered cyber–physical–human (CPH) system framework for human–robot collaborative assembly in flexible manufacturing and investigate the impact of modulating communications in the CPH system on system performance and human–robot interactions (HRIs). As the research method, we developed a one human–one robot hybrid cell where a human and a robot collaborated with each other to perform the assembly operation of different manufacturing components in a flexible manufacturing setup. We configured the human–robot collaborative system in three interconnected components of a CPH system: (i) cyber system, (ii) physical system, and (iii) human system. We divided the functions of the CPH system into three interconnected modules: (i) communication, (ii) computing or computation, and (iii) control. We derived a model to compute the human and robot’s bidirectional trust in each other in real time. We implemented the trust-triggered CPH framework on the human–robot collaborative assembly setup and modulated the communication methods among the cyber, physical, and human components of the CPH system in different innovative ways in three separate experiments. The research results show that modulating the communication methods triggered by bidirectional trust impacts on the effectiveness of the CPH system in terms of human–robot interactions, and task performance (efficiency and quality) differently. The results show that communication methods with an appropriate combination of a higher number of communication modes (cues) produces better HRIs and task performance. Based on a comparative study, it was concluded that the results prove the efficacy and superiority of configuring the HRC system in the form of a modular CPH system over using conventional HRC systems in terms of HRI and task performance. Configuring human–robot collaborative systems in the form of a CPH system can transform the design, development, analysis, and control of the systems and enhance their scope, ease, and effectiveness for various applications, such as industrial manufacturing, construction, transport and logistics, forestry, etc. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
19 pages, 9291 KiB  
Article
Vibration Control of Wheels in Distributed Drive Electric Vehicle Based on Electro-Mechanical Braking
by Yinggang Xu, Zheng Zhu, Zhaonan Li, Xiangyu Wang, Liang Li and Heng Wei
Machines 2025, 13(8), 730; https://doi.org/10.3390/machines13080730 (registering DOI) - 17 Aug 2025
Abstract
Electro-Mechanical Braking (EMB), as a novel brake-by-wire technology, is rapidly being implemented in vehicle chassis systems. Nevertheless, the integrated design of the EMB caliper contributes to an increased unsprung mass in Distributed Drive Electric Vehicles (DDEVs). Experimental results indicate that when the Anti-lock [...] Read more.
Electro-Mechanical Braking (EMB), as a novel brake-by-wire technology, is rapidly being implemented in vehicle chassis systems. Nevertheless, the integrated design of the EMB caliper contributes to an increased unsprung mass in Distributed Drive Electric Vehicles (DDEVs). Experimental results indicate that when the Anti-lock Braking System (ABS) is activated, these factors can induce high-frequency wheel oscillations. To address this issue, this study proposes an anti-oscillation control strategy tailored for EMB systems. Firstly, a quarter-vehicle model is established that incorporates the dynamics of the drive motor, suspension, and tire, enabling analysis of the system’s resonant behavior. The Discrete Fourier Transform (DFT) is applied to the difference between wheel speed and vehicle speed to extract the dominant frequency components. Then, an Adaptive Braking Intensity Field Regulation (ABIFR) strategy and a Model Predictive and Logic Control (MP-LC) framework are developed. These methods modulate the amplitude and frequency of braking torque reductions executed by the ABS to suppress high-frequency wheel oscillations, while ensuring sufficient braking force. Experimental validation using a real vehicle demonstrates that the proposed method increases the Mean Fully Developed Deceleration (MFDD) by 14.8% on low-adhesion surfaces and 15.2% on high-adhesion surfaces. Furthermore, the strategy significantly suppresses 12–13 Hz high-frequency oscillations, restoring normal ABS control cycles and enhancing both braking performance and ride comfort. Full article
(This article belongs to the Special Issue Advances in Dynamics and Control of Vehicles)
19 pages, 3976 KiB  
Article
Improving Centrifugal Pump Performance and Efficiency Using Composite Materials Through Additive Manufacturing
by Vasileios Papageorgiou, Gabriel Mansour and Ilias Chouridis
Machines 2025, 13(8), 729; https://doi.org/10.3390/machines13080729 (registering DOI) - 17 Aug 2025
Abstract
Additive Manufacturing is a rapidly developing technology that enables the fabrication of objects with complex geometries and high levels of customization while keeping the prototyping costs relatively low. In recent years, its application has grown to include the fabrication of end-use parts, creating [...] Read more.
Additive Manufacturing is a rapidly developing technology that enables the fabrication of objects with complex geometries and high levels of customization while keeping the prototyping costs relatively low. In recent years, its application has grown to include the fabrication of end-use parts, creating new opportunities in industries such as the automotive, aerospace, mechanical, and hydraulic engineering industries. The present research paper focuses on the fabrication and evaluation of 3D-printed operational end-use parts of a water pump, which were originally made from cast iron. This approach aims to determine whether AM can be an alternative for metal parts in operational systems such as water pumps. In particular, the impeller of a centrifugal pump is remanufactured using material extrusion AM technology with PPS-CF composite polymer as a fabrication material. Subsequently, the surface roughness of the two parts is measured, and the performance of each part is predicted by creating a CFD model. Additionally, the printed part is compared to the original part by conducting a centrifugal pump performance test for each impeller. The results show that the 3D-printed impeller achieves an approximate 15% increase in overall efficiency compared to the original impeller. Full article
(This article belongs to the Section Turbomachinery)
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28 pages, 4367 KiB  
Article
Design and Kinematic and Dynamic Analysis Simulation of a Biomimetic Parallel Mechanism for Lumbar Rehabilitation Exoskeleton
by Chao Hou, Zhicheng Yin, Di Wu, Rui Qian, Yu Tian and Hongbo Wang
Machines 2025, 13(8), 728; https://doi.org/10.3390/machines13080728 (registering DOI) - 16 Aug 2025
Abstract
Lumbar disc herniation is one of the primary causes of lower back pain, and its incidence has significantly increased with the development of industrialization. To assist in rehabilitation therapy, this paper proposes a flexible exoskeleton for active lumbar rehabilitation based on a 4-SPU/SP [...] Read more.
Lumbar disc herniation is one of the primary causes of lower back pain, and its incidence has significantly increased with the development of industrialization. To assist in rehabilitation therapy, this paper proposes a flexible exoskeleton for active lumbar rehabilitation based on a 4-SPU/SP biomimetic parallel mechanism. By analyzing the anatomical structure and movement mechanisms of the lumbar spine, a four degree of freedom parallel mechanism was designed to mimic the three-axis rotation of the lumbar spine around the coronal, sagittal, and vertical axes, as well as movement along the z-axis. Using a 3D motion capture system, data on the range of motion of the lumbar spine was obtained to guide the structural design of the exoskeleton. Using the vector chain method, the display equations for the drive joints of the mechanism were derived, and forward and inverse kinematic models were established and simulated to verify their accuracy. The dynamic characteristics of the biomimetic parallel mechanism were analyzed and simulated to provide a theoretical basis for the design of the exoskeleton control system. A prototype was fabricated and tested to evaluate its maximum range of motion and workspace. Experimental results showed that after wearing the exoskeleton, the lumbar spine’s range of motion could still reach over 83.5% of the state without the exoskeleton, and its workspace could meet the lumbar spine movement requirements for daily life, verifying the rationality and feasibility of the proposed 4-SPU/SP biomimetic parallel mechanism design. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
28 pages, 1222 KiB  
Review
Skyhook-Based Techniques for Vehicle Suspension Control: A Review of the State of the Art
by Jiyuan Wang, Zhenxing Huang, Haodong Hong, Siyao Yu, Weihan Shi and Xiaoliang Zhang
Machines 2025, 13(8), 727; https://doi.org/10.3390/machines13080727 - 15 Aug 2025
Abstract
Automotive suspension systems are key to improving ride comfort and handling stability. Over the past decades, active and semi-active suspensions have become a focal point in automotive engineering and have been widely adopted in the industry. Skyhook-based control and its related methodologies, as [...] Read more.
Automotive suspension systems are key to improving ride comfort and handling stability. Over the past decades, active and semi-active suspensions have become a focal point in automotive engineering and have been widely adopted in the industry. Skyhook-based control and its related methodologies, as a mature and viable solution, have been extensively implemented in vehicles. Despite the large number of research papers available on this topic, there remains a lack of comprehensive and up-to-date surveys in the literature that compare various Skyhook-based suspension control systems and their effectiveness. To bridge this gap, this paper systematically reviews the research progress in active and semi-active suspension controllers based on Skyhook principles over recent decades. Representative methods within major control rules are reported, and their characteristics, along with critical performance metrics, are critically analyzed. This paper also explores the development trends of Skyhook-based control. Full article
(This article belongs to the Special Issue Advances in Vehicle Suspension System Optimization and Control)
21 pages, 3047 KiB  
Article
Sensitivity Analysis of a Statistical Method for the Dynamic Coefficients Computations of a Tilting Pad Journal Bearing
by Michele Barsanti, Alberto Betti, Enrico Ciulli, Paola Forte and Matteo Nuti
Machines 2025, 13(8), 726; https://doi.org/10.3390/machines13080726 - 15 Aug 2025
Abstract
In this paper, an innovative method for the determination of the dynamic coefficients of tilting pad journal bearings (TPJBs) is described, and some of its characteristics are analyzed. The calculation is based on a parabolic modeling of the dependence of the dynamic coefficients [...] Read more.
In this paper, an innovative method for the determination of the dynamic coefficients of tilting pad journal bearings (TPJBs) is described, and some of its characteristics are analyzed. The calculation is based on a parabolic modeling of the dependence of the dynamic coefficients on the excitation frequency, on the estimation of the forces acting on the bearing as a function of the estimated displacements using a linear model and, finally, on the search for the best estimate of the parabola coefficients by minimizing the sum of the squares of the normalized residuals of displacements and forces on the bearings. The normalization is performed by dividing the deviations (between the measured values and those calculated by the model) by an estimate of the standard deviation of the force and displacement measurements. The results for a flooded tilting pad journal bearing, TPJB, are presented and compared with those obtained using traditional methods. The synchronous coefficients are also calculated and compared with those determined by linear interpolation. A preliminary statistical analysis of the sensitivity of the results to the variation in the standard deviation of the forces and displacements is presented. An extension of the model is proposed so that the coefficients of the optimal parabolas can be estimated as a function of the shaft rotation frequency. Full article
(This article belongs to the Special Issue Advances in Mechanism and Machine Science Within the IFIT 2024 Conference)
26 pages, 1481 KiB  
Article
Lagrangian Simulation of Sediment Erosion in Francis Turbines Using a Computational Tool in Python Coupled with OpenFOAM
by Mateo Narváez, Jeremy Guamán, Víctor Hugo Hidalgo, Modesto Pérez-Sánchez and Helena M. Ramos
Machines 2025, 13(8), 725; https://doi.org/10.3390/machines13080725 - 15 Aug 2025
Abstract
Hydraulic erosion from suspended sediment is a major degradation mechanism in Francis turbines of sediment-laden rivers, especially in Andean hydropower plants. This study presents a Python3.9-based computational tool integrating the empirical Oka erosion model within a Lagrangian particle tracking framework, coupled to single-phase [...] Read more.
Hydraulic erosion from suspended sediment is a major degradation mechanism in Francis turbines of sediment-laden rivers, especially in Andean hydropower plants. This study presents a Python3.9-based computational tool integrating the empirical Oka erosion model within a Lagrangian particle tracking framework, coupled to single-phase CFD in OpenFOAM 10. The novelty lies in a reduced-domain approach that omits the spiral casing and replicates its particle-induced swirl via a custom algorithm, lowering meshing complexity and computational cost while preserving erosion prediction accuracy. The method was applied to a full-scale Francis turbine at the San Francisco hydropower plant in Ecuador (nominal discharge 62.4 m3/s, rated output 115 MW, rotational speed 34.27 rad/s), operating under volcanic and erosive sediment loads. Maximum erosion rates reached ~1.2 × 10−4 mm3/kg, concentrated on runner blade trailing edges and guide vane pressure sides. Impact kinematics showed most collisions at near-normal angles (85°–98°, peak at 92°) and 6–9 m/s velocities, with rare 40 m/s impacts causing over 50× more loss than average. The workflow identifies critical wear zones, supports redesign and coating strategies, and offers a transferable, open-source framework for erosion assessment in turbines under diverse sediment-laden conditions. Full article
(This article belongs to the Special Issue Sustainable Manufacturing and Green Processing Methods, 2nd Edition)
20 pages, 2424 KiB  
Article
Predicting Vehicle-Engine-Radiated Noise Based on Bench Test and Machine Learning
by Ruijun Liu, Yingqi Yin, Yuming Peng and Xu Zheng
Machines 2025, 13(8), 724; https://doi.org/10.3390/machines13080724 - 15 Aug 2025
Viewed by 32
Abstract
As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency [...] Read more.
As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency of engine noise prediction and has the potential to serve as an alternative to traditional high-cost engine noise test methods. Experiments were conducted on a four-cylinder, four-stroke diesel engine, collecting surface vibration and radiated noise data under full-load conditions (1600–3000 r/min). Five prediction models were developed using support vector regression (SVR, including linear, polynomial, and radial basis function kernels), random forest regression, and multilayer perceptron, suitable for non-anechoic environments. The models were trained on time-domain and frequency-domain vibration data, with performance evaluated using the maximum absolute error, mean absolute error, and median absolute error. The results show that polynomial kernel SVR performs best in time domain modelling, with an average relative error of 0.10 and a prediction accuracy of up to 90%, which is 16% higher than that of MLP; the model does not require Fourier transform and principal component analysis, and the computational overhead is low, but it needs to collect data from multiple measurement points. The linear kernel SVR works best in frequency domain modelling, with an average relative error of 0.18 and a prediction accuracy of about 82%, which is suitable for single-point measurement scenarios with moderate accuracy requirements. Analysis of measurement points indicates optimal performance using data from the engine top between cylinders 3 and 4. This approach reduces reliance on costly anechoic facilities, providing practical value for noise control and design optimization. Full article
(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)
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7 pages, 199 KiB  
Editorial
Advances in Noise and Vibrations for Machines
by Lukasz Scislo, Davide Astolfi and Francesco Castellani
Machines 2025, 13(8), 723; https://doi.org/10.3390/machines13080723 - 14 Aug 2025
Viewed by 135
Abstract
Vibration analysis and monitoring are currently required in various fields of industry, from automotive and aeronautics to manufacturing and quality control, and from machining and maintenance to civil engineering [...] Full article
(This article belongs to the Special Issue Advances in Noise and Vibrations for Machines)
22 pages, 7761 KiB  
Article
Bearing-Weak-Fault Signal Enhancement and Diagnosis Based on Multivariate Statistical Hilbert Differential TEO
by Zhiqiang Liao, Renchao Cai, Zhijia Yan, Peng Chen and Xuewei Song
Machines 2025, 13(8), 722; https://doi.org/10.3390/machines13080722 - 13 Aug 2025
Viewed by 93
Abstract
The enhancement of weak-fault signal characteristics in rolling bearings under strong background noise interference has always been a challenging problem in rotating machinery fault diagnosis. Research indicates that multivariate statistical indicators such as skewness and kurtosis can characterize the fault features of vibration [...] Read more.
The enhancement of weak-fault signal characteristics in rolling bearings under strong background noise interference has always been a challenging problem in rotating machinery fault diagnosis. Research indicates that multivariate statistical indicators such as skewness and kurtosis can characterize the fault features of vibration signals. However, when the fault features in the signal are weak and severely affected by noise, the characterization capability of these indicators diminishes, significantly compromising diagnostic accuracy. To address this issue, this paper proposes a novel multivariate statistical filtering (MSF) method for multi-band filtering, which can effectively screen the target fault information bands in vibration signals during bearing faults. The core idea involves constructing a multivariate matrix of fused-fault multidimensional features by integrating fault and healthy signals, and then utilizing eigenvalue distance metrics to significantly characterize the spectral differences between fault and healthy signals. This enables the selection of frequency bands containing the most informative fault features from the segmented frequency spectrum. To address the inherent in-band residual noise in the MSF-processed signals, this paper further proposes the Hilbert differential Teager energy operator (HDTEO) based on MSF to suppress the filtered in-band noise, thereby enhancing transient fault impulses more effectively. The proposed method has been validated using both public datasets and laboratory datasets. Results demonstrate its effectiveness in accurately identifying fault characteristic frequencies, even under challenging conditions such as incipient bearing faults or severely weak vibration signatures caused by strong background noise. Finally, comparative experiments confirm the superior performance of the proposed approach. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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26 pages, 16083 KiB  
Article
Impact of the Magnetic Gap in Submerged Axial Flux Motors on Centrifugal Pump Hydraulic Performance and Internal Flow
by Qiyuan Zhu, Yandong Gu and Junjie Bian
Machines 2025, 13(8), 721; https://doi.org/10.3390/machines13080721 - 13 Aug 2025
Viewed by 169
Abstract
The integration of axial flux motors into canned motor pumps offers a promising approach to overcome the efficiency and size limitations of traditional designs, particularly in critical sectors like aerospace. However, the hydrodynamics in magnetic gap between the stator and rotor are poorly [...] Read more.
The integration of axial flux motors into canned motor pumps offers a promising approach to overcome the efficiency and size limitations of traditional designs, particularly in critical sectors like aerospace. However, the hydrodynamics in magnetic gap between the stator and rotor are poorly understood. This study investigates the effect of magnetic gap on performance and internal flow. Six magnetic gap schemes are developed, ranging from 0.2 to 1.2 mm. Numerical simulations are conducted, and simulation results showed good agreement with experimental data. The magnetic gap exhibits a non-linear effect on performance. The peak head coefficient occurs at a 0.4 mm gap and maximum efficiency at 1.0 mm. At a 0.2 mm gap, strong viscous shear forces increase disk friction loss and create high-vorticity flow. As the gap widens, flow transitions from viscosity-dominated to inertia-dominated, leading to a more ordered flow structure. The blade passing frequency is the dominant frequency. For a gap of 0.8 mm, the pressure fluctuation intensity is lowest. By analyzing performance, head coefficient, velocity, vorticity, entropy production, and pressure fluctuations, a gap of 0.8 mm is identified as the optimal design. This study provides critical guidance for optimizing the design of axial flux canned motor pumps. Full article
(This article belongs to the Special Issue Advanced Research and Development in Fluid Machinery: Design, Optimization, and Applications)
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13 pages, 3944 KiB  
Article
Design and Analysis of a Double-Three-Phase Permanent Magnet Fault-Tolerant Machine with Low Short-Circuit Current for Flywheel Energy Storage
by Xiaotong Li, Shaowei Liang, Buyang Qi, Zhenghui Zhao and Zhijian Ling
Machines 2025, 13(8), 720; https://doi.org/10.3390/machines13080720 - 13 Aug 2025
Viewed by 162
Abstract
This paper proposes a double-three-phase permanent magnet fault-tolerant machine (DTP-PMFTM) with low short-circuit current for flywheel energy storage systems (FESS) to balance torque performance and short-circuit current suppression. The key innovation lies in its modular winding configuration that ensures electrical isolation between the [...] Read more.
This paper proposes a double-three-phase permanent magnet fault-tolerant machine (DTP-PMFTM) with low short-circuit current for flywheel energy storage systems (FESS) to balance torque performance and short-circuit current suppression. The key innovation lies in its modular winding configuration that ensures electrical isolation between the two winding sets. First, the structural characteristics of the double three-phase windings are analyzed. Subsequently, the harmonic features of the resultant magnetomotive force (MMF) are systematically investigated. To verify the performance, the proposed machine is compared against a conventional winding structure as a baseline, focusing on key parameters such as output torque and short-circuit current. The experimental results demonstrate that the proposed machine achieves an average torque of approximately 14.7 N·m with a torque ripple of about 3.27%, a phase inductance of approximately 3.7 mH, and a short-circuit current of approximately 50.9 A. Crucially, compared to the conventional winding, the modular structure increases the phase inductance by about 32.1% and reduces the short-circuit current by 29.7%. Finally, an experimental platform is established to validate the performance of the machine. Full article
(This article belongs to the Special Issue High-Power-Density and High-Reliability Permanent Magnet Synchronous Machine Systems: Design, Control, Applications, and Sustainability)
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18 pages, 2645 KiB  
Article
Demonstration of a Condition Monitoring Scheme for a Locomotive Suspension System
by Xiaoyuan Liu and Adam Bevan
Machines 2025, 13(8), 719; https://doi.org/10.3390/machines13080719 - 12 Aug 2025
Viewed by 97
Abstract
A condition-based monitoring (CBM) system provides the possibility for the railway industry to guarantee reliability by executing prompt and low-cost maintenance. In this study, a simple model-based condition monitoring strategy for the railway vehicle suspension system is demonstrated. The method is based on [...] Read more.
A condition-based monitoring (CBM) system provides the possibility for the railway industry to guarantee reliability by executing prompt and low-cost maintenance. In this study, a simple model-based condition monitoring strategy for the railway vehicle suspension system is demonstrated. The method is based on a recursive least-square (RLS) algorithm regarding a deterministic parametric model. The fault detection approach for the locomotive suspension system is illustrated with three diagnostic modules. Multi-body simulation data are employed to validate the feasibility of this CBM strategy. The designed diagnostic model reveals that the suspension parameter estimates are consistent with the reference values. The corresponding demonstrator provides evidence that the monitoring system has potential applications and is suitable for further development. Full article
(This article belongs to the Special Issue Reliability Analysis and Fault Diagnosis of Safety-Critical Systems Using Data-Driven Approaches)
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16 pages, 3585 KiB  
Article
FedTP-NILM: A Federated Time Pattern-Based Framework for Privacy-Preserving Distributed Non-Intrusive Load Monitoring
by Chi Zhang, Biqi Liu, Xuguang Hu, Zhihong Zhang, Zhiyong Ji and Chenghao Zhou
Machines 2025, 13(8), 718; https://doi.org/10.3390/machines13080718 - 12 Aug 2025
Viewed by 135
Abstract
Existing non-intrusive load monitoring (NILM) methods predominantly rely on centralized models, which introduce privacy vulnerabilities and lack scalability in large industrial park scenarios equipped with distributed energy resources. To address this issue, a Federated Temporal Pattern-based NILM framework (FedTP-NILM) is proposed. It aims [...] Read more.
Existing non-intrusive load monitoring (NILM) methods predominantly rely on centralized models, which introduce privacy vulnerabilities and lack scalability in large industrial park scenarios equipped with distributed energy resources. To address this issue, a Federated Temporal Pattern-based NILM framework (FedTP-NILM) is proposed. It aims to ensure data privacy while enabling efficient load monitoring in distributed and heterogeneous environments, thereby extending the applicability of NILM technology in large-scale industrial park scenarios. First, a federated aggregation method is proposed, which integrates the FedYogi optimization algorithm with a secret sharing mechanism to enable the secure aggregation of local data. Second, a pyramid neural network architecture is presented to capture complex temporal dependencies in load identification tasks. It integrates temporal encoding, pooling, and decoding modules, along with an enhanced feature extractor, to better learn and distinguish multi-scale temporal patterns. In addition, a hybrid data augmentation strategy is proposed to expand the distribution range of samples by adding noise and linear mixing. Finally, experimental results validate the effectiveness of the proposed federated learning framework, demonstrating superior performance in both distributed energy device identification and privacy preservation. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring of Distributed Energy Equipment and Systems)
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17 pages, 10053 KiB  
Article
Characterization and Optimization of a Differential System for Underactuated Robotic Grippers
by Sebastiano Angelella, Virginia Burini, Silvia Logozzo and Maria Cristina Valigi
Machines 2025, 13(8), 717; https://doi.org/10.3390/machines13080717 - 12 Aug 2025
Viewed by 178
Abstract
This paper delves into the potential of an optimized differential system within an underactuated tendon-driven soft robotic gripper, a crucial component that enhances the grasping abilities by allowing fingers to secure objects adapting to different shapes and geometries. The original version of the [...] Read more.
This paper delves into the potential of an optimized differential system within an underactuated tendon-driven soft robotic gripper, a crucial component that enhances the grasping abilities by allowing fingers to secure objects adapting to different shapes and geometries. The original version of the differential system exhibited a certain degree of deformability, which introduced some functional advantages. In particular, its flexibility allowed for more delicate grasping operations by acting as a force reducer and enabling a more gradual application of contact forces, an essential feature when handling fragile objects. Nonetheless, while these benefits are noteworthy, a rigid differential remains more effective for achieving firm and secure grasps. The primary goal of this study is to analyze the differential’s performance through FEM simulations and deformation experiments, assessing its structural behavior under various conditions. Additionally, the research explores an innovative differential geometry aimed at striking the ideal balance, ensuring a robust grasp while retaining a controlled degree of deformability. By refining the differential’s design, this study seeks to enhance the efficiency of underactuated soft robotic grippers, ultimately enhancing their capabilities in handling diverse objects ensuring a compliant and secure grasp with optimized efficiency. Full article
(This article belongs to the Special Issue Advances in Mechanism and Machine Science Within the IFIT 2024 Conference)
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16 pages, 1473 KiB  
Article
Experimental Analysis of a Coaxial Magnetic Gear Prototype
by Stefano Lovato, Giovanni Barosco, Ludovico Ortombina, Riccardo Torchio, Piergiorgio Alotto, Maurizio Repetto and Matteo Massaro
Machines 2025, 13(8), 716; https://doi.org/10.3390/machines13080716 - 12 Aug 2025
Viewed by 85
Abstract
Magnetic gears are becoming promising devices that can replace conventional mechanical gears in several applications, where reduced maintenance, absence of lubrication and intrinsic overload protection are especially relevant. This paper focuses on the experimental analysis of a coaxial magnetic gear prototype recently developed [...] Read more.
Magnetic gears are becoming promising devices that can replace conventional mechanical gears in several applications, where reduced maintenance, absence of lubrication and intrinsic overload protection are especially relevant. This paper focuses on the experimental analysis of a coaxial magnetic gear prototype recently developed at the Department of Industrial Engineering of the University of Padova. It is found that its efficiency is high and aligned with prototypes in the literature, its stationary response confirms the velocity ratio of the corresponding mechanical planetary gear, the overload protection is aligned with numerical prediction, while the dynamic response highlights that the intrinsic compliance of the magnetic coupling prevents the use of such device in high-frequency transients. It is concluded that the proposed architecture can be effectively employed for speed reducers applications where low-frequency modulation is sufficient, which includes many industrial applications. Nevertheless, high rotational speeds are allowed. The performance characteristics, although specific for the prototype considered, experimentally highlights the key features of coaxial magnetic gear devices. The experimental performance are also compared with estimations from the literature, when available. Full article
(This article belongs to the Special Issue Dynamics and Lubrication of Gears)
21 pages, 1206 KiB  
Article
Event-Triggered H∞ Control for Permanent Magnet Synchronous Motor via Adaptive Dynamic Programming
by Cheng Gu, Hanguang Su, Wencheng Yan and Yi Cui
Machines 2025, 13(8), 715; https://doi.org/10.3390/machines13080715 - 12 Aug 2025
Viewed by 172
Abstract
In this work, an adaptive dynamic programming (ADP)-based event-triggered infinite-horizon (H∞) control algorithm is proposed for high-precision speed regulation of permanent magnet synchronous motors (PMSMs). The H∞ control problem of PMSM can be formulated as a two-player zero-sum differential [...] Read more.
In this work, an adaptive dynamic programming (ADP)-based event-triggered infinite-horizon (H∞) control algorithm is proposed for high-precision speed regulation of permanent magnet synchronous motors (PMSMs). The H∞ control problem of PMSM can be formulated as a two-player zero-sum differential game, and only a single critic neural network is needed to approximate the solution of the Hamilton–Jacobi–Isaacs (HJI) equations online, which significantly simplifies the control structure. Dynamically balancing control accuracy and update frequency through adaptive event-triggering mechanism significantly reduces the computational burden. Through theoretical analysis, the system state and critic weight estimation error are rigorously proved to be uniform ultimate boundedness, and the Zeno behavior is theoretically precluded. The simulation results verify the high accuracy tracking capability and the strong robustness of the algorithm under both load disturbance and shock load, and the event-triggering mechanism significantly reduces the computational resource consumption. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring of Distributed Energy Equipment and Systems)
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19 pages, 619 KiB  
Review
Condition-Based Maintenance in Complex Degradation Systems: A Review of Modeling Evolution, Multi-Component Systems, and Maintenance Strategies
by Hui Cao, Jie Yu and Fuhai Duan
Machines 2025, 13(8), 714; https://doi.org/10.3390/machines13080714 - 12 Aug 2025
Viewed by 324
Abstract
This review systematically examines the evolution of maintenance strategies for complex systems, with a focus on the advancements in condition-based maintenance (CBM) decision-making methodologies. Traditional approaches, such as post-failure maintenance and time-based maintenance, are increasingly supplanted by CBM due to challenges like high [...] Read more.
This review systematically examines the evolution of maintenance strategies for complex systems, with a focus on the advancements in condition-based maintenance (CBM) decision-making methodologies. Traditional approaches, such as post-failure maintenance and time-based maintenance, are increasingly supplanted by CBM due to challenges like high costs or inefficiency in resource allocation. CBM leverages system reliability models in conjunction with component degradation data to dynamically establish maintenance thresholds, optimizing resource utilization while minimizing operational risks and repair costs. Research has expanded from single-component degradation systems to multi-component systems, leveraging degradation models and optimization algorithms to propose strategies addressing multi-level control limits, economic dependencies, and task constraints. Recent studies emphasize multi-component interactions, incorporating structural influences, imperfect repairs, and economic correlations into maintenance planning. Despite progress, challenges persist in modeling coupled degradation mechanisms and coordinating maintenance decisions for interdependent components. Future research directions should encompass adaptive learning strategies for dynamic degradation processes, such as those employed in intelligent agents for real-time environmental adaptation, and the incorporation of intelligent predictive technologies to enhance system performance and resource utilization. Full article
(This article belongs to the Special Issue Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines)
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28 pages, 2224 KiB  
Review
Enhancing Accuracy of Ultrasonic Transit-Time Flow Measurement in Hydropower Systems Under Complex Operating Conditions: A Comprehensive Review
by Lin Li, Ye Zhou, Beibei Xu, Hongli Zhao and Yuntao Ye
Machines 2025, 13(8), 713; https://doi.org/10.3390/machines13080713 - 11 Aug 2025
Viewed by 196
Abstract
High-precision measurement of water turbine flow is critical for ensuring the stable operation of hydropower stations and enhancing power generation efficiency. Ultrasonic transit-time flow meters, owing to their non-intrusive measurement capability and robust environmental adaptability, have gained widespread application in flow monitoring within [...] Read more.
High-precision measurement of water turbine flow is critical for ensuring the stable operation of hydropower stations and enhancing power generation efficiency. Ultrasonic transit-time flow meters, owing to their non-intrusive measurement capability and robust environmental adaptability, have gained widespread application in flow monitoring within hydropower settings. However, under complex operating conditions, their measurement accuracy remains susceptible to constraints imposed by installation environments, construction quality, and intrinsic device performance limitations. This review systematically examines the fundamental principles, system architecture, and typical classifications of ultrasonic transit-time flow meters for flow measurement. It critically evaluates key techniques for field deployment and methodologies for the accurate acquisition of geometric parameters. A primary focus lies in synthesizing and categorizing the principal sources of error affecting measurement accuracy, alongside an analysis of their underlying causes. Building upon this analysis, the review explores and summarizes current key technological pathways and engineering solutions aimed at enhancing ultrasonic transit-time flow meters’ measurement precision. Furthermore, it critically assesses the associated application challenges and emerging development trends (exploration of cutting-edge directions). Collectively, this work offers comprehensive theoretical reference and technical guidance to support the high-reliability application and optimized design of ultrasonic transit-time flow meters within the complex environments characteristic of hydropower stations. Full article
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26 pages, 1505 KiB  
Article
A Two-Stage Deep-Learning Framework for Industrial Anomaly Detection: Integrating Small-Sample Semantic Segmentation and Knowledge Distillation
by Lei Guo and Feiya Lv
Machines 2025, 13(8), 712; https://doi.org/10.3390/machines13080712 - 11 Aug 2025
Viewed by 398
Abstract
This paper addresses the challenges of anomaly detection in industrial components by proposing a two-stage deep-learning approach combining semantic segmentation and knowledge distillation. Traditional methods, such as manual inspection and machine vision, face limitations in efficiency and accuracy when dealing with complex defects. [...] Read more.
This paper addresses the challenges of anomaly detection in industrial components by proposing a two-stage deep-learning approach combining semantic segmentation and knowledge distillation. Traditional methods, such as manual inspection and machine vision, face limitations in efficiency and accuracy when dealing with complex defects. To overcome these issues, we first introduce a small-sample semantic segmentation model based on a U-Net architecture, enhanced with an Adaptive Multi-Scale Attention Module (AMAM) and gate attention mechanisms to improve edge detection and multi-scale feature extraction. The second stage employs a knowledge distillation-based anomaly detection model, where a pre-trained teacher network (WideResNet50) extracts features, and a student network reconstructs them, with differences indicating anomalies. A Transformer-based feature aggregation module further refines the process. Experiments on the MVTec dataset demonstrate superior performance, with the segmentation model achieving 96.4% mIoU and the anomaly detection model attaining 98.3% AUC, outperforming State-of-the-Art methods. Under an extremely small-sample regime of merely 27 training images, the proposed model still attains a mIoU exceeding 94%. The two-stage approach significantly enhances detection accuracy by reducing background interference and focusing on localized defects. This work contributes to industrial quality control by improving efficiency, reducing false positives, and adapting to limited annotated data. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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37 pages, 3983 KiB  
Review
Fault Diagnosis of In-Wheel Motors Used in Electric Vehicles: State of the Art, Challenges, and Future Directions
by Yukun Tao, Xuan Wang, Liang Zhang, Xiaoyi Bao, Hongtao Xue, Huiyu Yue, Huayuan Feng and Dongpo Yang
Machines 2025, 13(8), 711; https://doi.org/10.3390/machines13080711 - 11 Aug 2025
Viewed by 150
Abstract
In-wheel motors (IWMs) have become a promising solution for electric vehicles due to their compact design, high integration, and flexible torque control. However, their exposure to harsh operating conditions increases the risk of mechanical, electrical, and magnetic faults, making reliable fault diagnosis essential [...] Read more.
In-wheel motors (IWMs) have become a promising solution for electric vehicles due to their compact design, high integration, and flexible torque control. However, their exposure to harsh operating conditions increases the risk of mechanical, electrical, and magnetic faults, making reliable fault diagnosis essential for ensuring driving safety and system reliability. Although considerable progress has been made in fault diagnosis techniques related to IWMs, a systematic review in this area is still lacking. To address this gap, this paper provides a comprehensive review of fault diagnosis techniques for IWMs. First, typical faults in IWMs are analyzed with a focus on their unique structural and failure characteristics. Then, the applications and recent research progress of three major categories of fault diagnosis approaches—model-based, signal-based, and knowledge-based methods—in the context of IWMs are critically reviewed. Finally, key challenges and pain points in IWM diagnosis are discussed, along with promising future research directions. Full article
(This article belongs to the Special Issue New Journeys in Vehicle System Dynamics and Control)
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24 pages, 5372 KiB  
Article
An Integrated Path Planning and Tracking Framework Based on Adaptive Heuristic JPS and B-Spline Optimization
by Zhaoran Sun, Qiang Luo, Zhengwei Zhang, Yao Peng, Quan Liu, Shijie Zheng and Jiukun Liu
Machines 2025, 13(8), 710; https://doi.org/10.3390/machines13080710 - 11 Aug 2025
Viewed by 179
Abstract
In this paper, we propose a navigation synthesis method for indoor mobile robots based on the Improved Jumping Point Search (JPS) framework. Although traditional JPS has high search efficiency, it often leads to excessive node expansion and sharp turns in complex environments, which [...] Read more.
In this paper, we propose a navigation synthesis method for indoor mobile robots based on the Improved Jumping Point Search (JPS) framework. Although traditional JPS has high search efficiency, it often leads to excessive node expansion and sharp turns in complex environments, which limits its practical application. In order to overcome these problems, we introduced three key strategies. First, we used a density-sensing heuristic function calculated by integrating the image to improve the adaptability of complex areas. Secondly, we extracted structural key points from the path and used third-order B-splines to fit them to enhance smoothness and continuity. Third, a curvature-driven Regulated Pure Pursuit (RPP) controller adjusts the look-ahead distance and speed based on path curvature, improving tracking stability. Simulation results show that the proposed method reduces planning time and node redundancy while generating smoother and more executable paths than the conventional JPS framework. Full article
(This article belongs to the Section Automation and Control Systems)
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21 pages, 4663 KiB  
Article
Temporal Margins and Behavioral Features for Early Risk Assessment in Left-Turn Vehicle and Bicycle Conflicts at Signalized Intersections
by Shuncong Shen, Mitsuki Hashimoto, Shoko Oikawa, Yasuhiro Matsui and Toshiya Hirose
Machines 2025, 13(8), 709; https://doi.org/10.3390/machines13080709 - 10 Aug 2025
Viewed by 295
Abstract
Between 2019 and 2023, left-turn crashes accounted for 4.5% of traffic accidents in Japan, with 36% of injuries involving cyclists and 66% at signalized intersections. This study quantifies conflict situations between left-turning vehicles and straight-moving bicycles in real-world traffic environments and provides a [...] Read more.
Between 2019 and 2023, left-turn crashes accounted for 4.5% of traffic accidents in Japan, with 36% of injuries involving cyclists and 66% at signalized intersections. This study quantifies conflict situations between left-turning vehicles and straight-moving bicycles in real-world traffic environments and provides a foundation for determining appropriate timing of future in-vehicle early warning systems. Trajectories reconstructed from seven hours of camera footage yielded six spatio-temporal and behavioral indicators for 37 events with a post-encroachment time (PET) ≤ 3 s. Indicators—PET, time-to-crossing (TTC), right-of-way, urgent braking, deceleration to avoid a crash, and Kalman-based trajectory variance—were statistically related to a composite risk index, R. Approximately 80% of events fell within PETs of 2–3 s, while urgent braking occurred in 50% of cases with PETs of ≤2 s. Each 1 s reduction in PET increased R by 0.18 (R2 = 0.55). PETs ≤ 2.5 s or TTCs ≤ 1.5 s flagged 95% of high-risk events 0.5 s in advance. Joint thresholds involving urgent braking and high variance raised coverage to 100%, with lead times of 0–1.4 s and a false alarm rate of 8%. These findings provide an innovative multi-indicator framework based on real-world trajectories, offering quantitative scenario-specific thresholds for effective in-vehicle warnings at urban intersections. Full article
(This article belongs to the Section Vehicle Engineering)
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20 pages, 6192 KiB  
Article
A Data-Driven Fault Diagnosis Method for Marine Steam Turbine Condensate System Based on Deep Transfer Learning
by Yuhui Liu, Liping Chen, Duansen Shangguan and Chengcheng Yu
Machines 2025, 13(8), 708; https://doi.org/10.3390/machines13080708 - 10 Aug 2025
Viewed by 255
Abstract
Accurate fault diagnosis in marine steam turbine condensate systems is challenged by insufficient real fault samples and dynamic operational conditions. To address this limitation, DTL-DFD, a novel framework integrating digital twins (DTs) and deep transfer learning (DTL), is proposed, wherein a high-fidelity physics-constrained [...] Read more.
Accurate fault diagnosis in marine steam turbine condensate systems is challenged by insufficient real fault samples and dynamic operational conditions. To address this limitation, DTL-DFD, a novel framework integrating digital twins (DTs) and deep transfer learning (DTL), is proposed, wherein a high-fidelity physics-constrained digital twin model is constructed through the systematic injection of six diagnostic classes (1 normal + 5 faults), including insufficient circulation water flow.Through an innovative all-layer parameter initialization with a partial fine-tuning (ALPT-PF) strategy, all weights and biases from a pre-trained one-dimensional convolutional neural network (1D-CNN) were fully transferred to the target model, which was subsequently fine-tuned via a hierarchical learning rate mechanism to adapt to real-world distribution discrepancies. Experimental results demonstrate 94.34% accuracy on cross-distribution test sets with a 4.72% improvement over state-of-the-art methods, confirming significant enhancements in generalization capability and diagnostic stability under small-sample conditions with significant real data reduction, thereby providing an effective solution for the intelligent operation and maintenance of marine steam turbine systems. Full article
(This article belongs to the Special Issue Advancements in Mechanical Power Transmission and Its Elements: Second Edition)
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13 pages, 1530 KiB  
Article
Interval Observer for Vehicle Sideslip Angle Estimation Using Extended Kalman Filters
by Fernando Viadero-Monasterio, Miguel Meléndez-Useros, Basilio Lenzo and Beatriz López Boada
Machines 2025, 13(8), 707; https://doi.org/10.3390/machines13080707 - 9 Aug 2025
Viewed by 237
Abstract
Accurate estimation of the vehicle sideslip angle is critical for the effective operation of advanced driver assistance systems and active safety functions such as electronic stability control. However, direct measurement of sideslip angle is impractical in series-production vehicles due to high sensor cost. [...] Read more.
Accurate estimation of the vehicle sideslip angle is critical for the effective operation of advanced driver assistance systems and active safety functions such as electronic stability control. However, direct measurement of sideslip angle is impractical in series-production vehicles due to high sensor cost. Furthermore, existing estimation methods often neglect the impact of model uncertainties on estimation error, which can compromise estimation reliability and, consequently, vehicle stability. To address these limitations, this paper proposes an interval observer based on a Kalman filter that accounts explicitly for model uncertainties in the sideslip angle estimation process. The proposed method generates both upper and lower bounds of the estimated sideslip angle, providing a quantifiable measure of uncertainty that enhances the robustness of control systems that depend on this measurement. Given the limitations of simplified vehicle models, a combined vehicle roll and lateral dynamics model is utilized to improve estimation accuracy. The effectiveness of the proposed methodology is demonstrated through a series of simulation experiments conducted using CarSim. Full article
(This article belongs to the Special Issue Vehicle Dynamics Estimation and Fault Monitoring)
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32 pages, 17410 KiB  
Article
An Improved Black-Winged Kite Algorithm for High-Accuracy Parameter Identification of a Photovoltaic Double Diode Model
by Quanru Chen, Kun Ding, Xiang Chen, Zenan Yang, Mingkang Xu and Fei Teng
Machines 2025, 13(8), 706; https://doi.org/10.3390/machines13080706 - 9 Aug 2025
Viewed by 214
Abstract
This study proposes an improved Black-Winged Kite Algorithm (SRQ-BKA) for accurate parameter identification of the photovoltaic (PV) double diode model (DDM). The proposed method integrates three key mechanisms: specular reflection learning (SRL) to improve initial population diversity, preventing premature convergence and enabling a [...] Read more.
This study proposes an improved Black-Winged Kite Algorithm (SRQ-BKA) for accurate parameter identification of the photovoltaic (PV) double diode model (DDM). The proposed method integrates three key mechanisms: specular reflection learning (SRL) to improve initial population diversity, preventing premature convergence and enabling a more comprehensive exploration of the solution space for optimal parameters; soft rime search (SRS) to balance global exploration and local exploitation, ensuring efficient identification by dynamically adjusting the search focus; and quadratic interpolation (QI) to accelerate convergence by fine-tuning the search toward optimal parameters, enhancing accuracy and speeding up the identification process. The root mean square error (RMSE) is employed as the objective function to minimize the error between the measured and predicted I-V characteristics of the PV module. Experimental results demonstrate that the SRQ-BKA outperforms other algorithms, achieving a minimum RMSE of 0.00262 A for the DDM and exhibiting strong stability, as evidenced by an average RMSE of 0.00278 A across 1000 runs. The method also demonstrates excellent parameter identification accuracy for both the single diode model (SDM) and triple diode model (TDM), further validating its robustness and practical applicability. Full article
(This article belongs to the Topic Energy Systems: Design, Management, Control and Monitoring)
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31 pages, 5103 KiB  
Article
Multi-Objective Optimization of Battery Pack Mounting System for Construction Machinery
by Dunhuang Lin, Run Sun, Hai Wei and Yujiang Wang
Machines 2025, 13(8), 705; https://doi.org/10.3390/machines13080705 - 9 Aug 2025
Viewed by 216
Abstract
With the accelerated electrification of engineering machinery, the battery pack mounting system plays a critical role in enhancing the vehicle’s structural safety and vibration-damping performance. This paper proposes an optimization framework for the multi-layer battery pack mounting systems used in such machinery. The [...] Read more.
With the accelerated electrification of engineering machinery, the battery pack mounting system plays a critical role in enhancing the vehicle’s structural safety and vibration-damping performance. This paper proposes an optimization framework for the multi-layer battery pack mounting systems used in such machinery. The framework integrates a multi-degree-of-freedom (MDOF) dynamic model, uncertainty analysis, and a multi-objective evolutionary algorithm (MOEA) to resolve the vibration suppression challenges associated with large-mass battery packs under harsh operating conditions. A parameter optimization method is introduced with the objectives of increasing natural frequencies, enhancing modal decoupling, and avoiding resonance. By identifying key influencing parameters and performing a comprehensive optimization of mount locations and stiffness, this approach achieves a highly efficient improvement in dynamic performance. Simulation and analysis results demonstrate that, compared to the initial design, the proposed method significantly elevates the system’s first six natural frequencies (by 13.6%, 7.8%, 3.3%, 2.5%, 11.7%, and 9.4%, respectively). Furthermore, it enhances the energy decoupling between modes, with the decoupling rates for Y-direction translation and Z-axis rotation both increasing by 11.3%. This achieves a synergistic improvement in the system’s vibration avoidance and decoupling performance. The methodology offers an effective means to optimize the safety and operational stability of battery systems in electric engineering machinery. Full article
(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)
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19 pages, 3156 KiB  
Review
On the Lightning Attachment Process of Wind Turbine–Observation, Experiments and Modelling
by Zixin Guo, Wah Hoon Siew, Qingmin Li and Weidong Shi
Machines 2025, 13(8), 704; https://doi.org/10.3390/machines13080704 - 9 Aug 2025
Viewed by 268
Abstract
Wind power plays an increasingly important role in power generation as one of the most popular renewable energy sources. With the increasing capacity and height of wind turbines, lightning incidents have become one of the most serious threats to wind turbines, especially for [...] Read more.
Wind power plays an increasingly important role in power generation as one of the most popular renewable energy sources. With the increasing capacity and height of wind turbines, lightning incidents have become one of the most serious threats to wind turbines, especially for wind turbine blades. It is important to fully understand the lightning attachment process of a wind turbine, which is not only essential to academic research but also useful to the design of a lightning protection system. Plenty of work has been conducted from three main perspectives: field observations, laboratory experiments, and simulation models. In this paper, the existing research achievements have been reviewed, and problems to be solved have been proposed. The monitoring of lightning incidents on wind farms remains a challenge, and a device to capture lightning strikes to wind turbines with high efficiency is in demand. The impact of sensors on the blade on the lightning protection system cannot be ignored and needs further investigation. For the simulation model, the influence of space charge on the lightning attachment process is not fully understood, and improvements might be made to the existing model. Full article
(This article belongs to the Section Electrical Machines and Drives)
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16 pages, 2890 KiB  
Article
Thermal Behavior Improvement in Induction Motors Using a Pulse-Width Phase Shift Triangle Modulation Technique in Multilevel H-Bridge Inverters
by Francisco M. Perez-Hidalgo, Juan-Ramón Heredia-Larrubia, Antonio Ruiz-Gonzalez and Mario Meco-Gutierrez
Machines 2025, 13(8), 703; https://doi.org/10.3390/machines13080703 - 8 Aug 2025
Viewed by 152
Abstract
This study investigates the thermal performance of induction motors powered by multilevel H-bridge inverters using a novel pulse-width phase shift triangle modulation (PSTM-PWM) technique. Conventional PWM methods introduce significant harmonic distortion, increasing copper and iron losses and causing overheating and reduced motor lifespan. [...] Read more.
This study investigates the thermal performance of induction motors powered by multilevel H-bridge inverters using a novel pulse-width phase shift triangle modulation (PSTM-PWM) technique. Conventional PWM methods introduce significant harmonic distortion, increasing copper and iron losses and causing overheating and reduced motor lifespan. Through experimental testing and comparison with standard PWM techniques (LS-PWM and PS-PWM), the proposed PSTM-PWM reduces harmonic distortion by up to 64% compared to the worst one and internal motor losses by up to 5.5%. A first-order thermal model is used to predict motor temperature, validated with direct thermocouple measurements and infrared thermography. The results also indicate that the PSTM-PWM technique improves thermal performance, particularly at a triangular waveform peak value of 3.5 V, reducing temperature by around 6% and offering a practical and simple solution for industrial motor drive applications. The modulation order was set to M = 7 to reduce both the losses in the power inverter and to prevent the generation of very high voltage pulses (high dV/dt), which can deteriorate the insulation of the induction motor windings over time. Full article
(This article belongs to the Special Issue Advances in Power Electronics for Electromechanical Energy Conversion and Drive Systems)
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41 pages, 5164 KiB  
Review
Electric Vehicle Motors Free of Rare-Earth Elements—An Overview
by Shriram Srinivasarangan Rangarajan, Chandan Kumar Shiva, Edward Randolph Collins and Tomonobu Senjyu
Machines 2025, 13(8), 702; https://doi.org/10.3390/machines13080702 - 8 Aug 2025
Viewed by 244
Abstract
Electric vehicles offer a promising alternative to traditional internal combustion engine vehicles, mitigating air and noise pollution while reducing reliance on petroleum resources. However, the widespread adoption of electric vehicles faces several challenges, including high upfront costs, limited driving range, and the availability [...] Read more.
Electric vehicles offer a promising alternative to traditional internal combustion engine vehicles, mitigating air and noise pollution while reducing reliance on petroleum resources. However, the widespread adoption of electric vehicles faces several challenges, including high upfront costs, limited driving range, and the availability of charging infrastructure. The shift toward electric vehicle motors that do not rely on rare-earth elements is an important and massive engineering undertaking. Permanent magnet synchronous motors, which use copper windings instead of permanent magnets to generate the excitation field, offer an alternative approach to reducing rare-earth material usage, with research focusing on optimizing their design and control for electric vehicle applications. Induction motors are being reconsidered for the majority of electric vehicle models due to their robust design, established manufacturing infrastructure, and absence of rare-earth magnets, offering a viable alternative with ongoing research focused on improving their efficiency and power density. New electric vehicle (EV) motors using rotors outfitted with electromagnets (i.e., wire coils) are perhaps the most promising near-term solution for producing powerful motors without REEs altogether. This paper presents an overview of electric vehicles with the possible inclusion of rare-earth-free elements. Full article
(This article belongs to the Special Issue Wound Field and Less Rare-Earth Electrical Machines in Renewables)
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