IEEE Open Journal of the Industrial Electronics Society最新文献

{"title":"Improved Predictive Torque Control for SRM Drives Based on a Global Integral Sliding Mode Speed Controller With Extended State Observer","authors":"Ping Ren;Jingwei Zhu;Xinying Miao;Jiankang Zhong;Huatang Sun","doi":"10.1109/OJIES.2026.3670699","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3670699","url":null,"abstract":"This article proposes a novel dual-loop control strategy to comprehensively improve the torque and speed performance of switched reluctance motor drives. For the torque control loop, a predictive torque control with sector adaptive allocation technology (PTC-SAAT) is designed to suppress torque ripple and eliminate negative torque. For the speed control loop, a global integral sliding mode speed controller enhanced by an extended state observer (GISMSC-ESO) is proposed, integrating a global sliding surface for robust tracking, an improved exponential reaching law for chattering suppression, and an extended state observer for real-time disturbance compensation. The two loops are cascaded to form a cohesive control architecture. Simulation and experimental results demonstrate that the proposed PTC-SAAT-GISMSC-ESO algorithm achieves superior dynamic and steady-state performance: it provides at least 11% faster speed response and approximately 15% reduced speed-loop chattering compared to traditional methods, while simultaneously maintaining low torque ripple and strong robustness against load and speed variations.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"734-746"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11424344","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synchronized IoT Framework for Vibration-Based Structural Health Monitoring of Suspension Bridges","authors":"Muhammad Hassan;Amr A. Nassr;Mohamed AbdelRaheem","doi":"10.1109/OJIES.2026.3706609","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3706609","url":null,"abstract":"This article presents the design, implementation, and experimental validation of an Internet-of-Things (IoT)-based structural health monitoring (SHM) system for suspension bridges using synchronized vibration measurements. The proposed framework integrates distributed micro-electro-mechanical systems (MEMSs)-based sensing nodes, global positioning system (GPS)-assisted time synchronization, low-power wireless communication, and cloud-based modal analysis to support continuous monitoring of structural conditions. The main contribution of this work is the end-to-end integration of synchronized low-power IoT sensing, multitier wireless communication, and cloud-based vibration analysis within a unified SHM framework, together with experimental validation on a laboratory-scale suspension bridge. In the proposed system, each leaf node incorporates a high-accuracy digital accelerometer, ESP-NOW communication, and a duty-cycled sleep mode to reduce power consumption, while a central gateway aggregates the acquired data and transmits them to a remote server through a cellular network. Natural-frequency analysis, mode-shape evaluation, and the modal assurance criterion (MAC) are employed to assess changes in structural dynamic behavior. The system was evaluated using a laboratory-scale suspension bridge instrumented with five synchronized IoT sensing nodes and subjected to controlled shake-table excitations. Four hanger- and main-cable-damage scenarios were introduced to evaluate the overall operation of the proposed framework. The results demonstrate successful synchronized vibration acquisition, reliable wireless data transmission, and the ability to capture damage-sensitive modal changes, including natural-frequency shifts, mode-shape distortions, and MAC variations under different damage conditions. Communication experiments further show that ESP-NOW reduces energy consumption compared with transmission control protocol/internet protocol (TCP/IP) over Wi-Fi, extending node battery lifetime by approximately 40% through sleep-mode operation. Overall, the proposed SHM framework provides a cost-effective, energy-efficient, and synchronized IoT solution for vibration-based monitoring of suspension bridges under laboratory conditions. Future work will focus on field validation on real bridge structures and long-term operation under practical environmental conditions.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"1051-1067"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11575067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148490913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Semiconductor Defect Classification in SEM Images via Domain Adaptation","authors":"Dongyoon Yang;Jimoon Jeong;Hyunsoo Yoon","doi":"10.1109/OJIES.2026.3663382","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3663382","url":null,"abstract":"Automated defect classification in semiconductor manufacturing is a critical task, where various types of defects need to be identified and classified. With the increasing complexity of semiconductor devices, defect classification has become a challenging problem, especially when dealing with limited labeled data. However, in real-world scenarios, labeled data are often imbalanced and limited, making it challenging to develop accurate models. Domain adaptation is a technique that enables models to adapt to new domains with limited labeled data. In this article, we propose the margin–margin discrepancy distance (M<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>DD) algorithm, which replaces cross-entropy with margin cross-entropy, allowing the minority class's decision boundary to be expanded and robustly separated from the majority class. We evaluate M<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>DD on four domain adaptation tasks and demonstrate its superiority in defect classification tasks with sparse labeled data in the target domain. Our results show that M<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>DD outperforms traditional ERM, which relies on spurious features and existing margin discrepancy distance, by effectively capturing core features. This makes M<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>DD a reliable approach for real-world applications in semiconductor defect classification.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"766-779"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11389175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized Pulse Patterns Design for Electrical Drives: A Reinforcement Learning Approach","authors":"Mohammad Abu-Ali;Felix Berkel;Maximilian Manderla;Markus Pietschner;Daniel Görges","doi":"10.1109/OJIES.2026.3692225","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3692225","url":null,"abstract":"Optimal modulation techniques, such as optimized pulse patterns (OPPs) are essential for minimizing power losses and hence achieving efficient operation of electrical drive systems. Conventional OPPs are designed offline to minimize different steady-state performance criteria and require switching angles-based analytical models suitable for gradient-based optimization. Such models are, however, not always available and can be complex and difficult to validate. Hence, a data-driven design approach that can utilize any form of models, or even be used for direct OPP learning, e.g., from a real-world drive, without requiring any modeling effort, is of significant interest. In this article, we present a first proof of concept for a data-driven design of steady-state OPPs using reinforcement learning (RL). We explore the feasibility of training an end-to-end neural network policy that maps desired working points to reference OPPs by interacting with an electrical drive environment. We introduce a novel parametrization of switching angles that inherently incorporates their constraints, and we utilize an efficient simulation setup to simplify the training for this proof-of-concept investigation. Through extensive simulation and real-world experiments, we demonstrate the potential and tradeoffs of the proposed RL-based approach for designing loss-optimal pulse patterns for a traction permanent magnet synchronous motor drive. The performance is benchmarked against a conventional, indirectly loss-optimized, model-based OPP design. Test bench results show a potential for power loss reduction in learned OPPs compared to conventional ones, despite the simulation model bias, and motivate learning OPPs directly from a real-world drive environment.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"905-919"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11514993","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Label-Consistent Input Structuring Based on Second-Stage Sliding Window for PMSM Fault Diagnosis","authors":"Thanh Son Nguyen;Pirat Khunkitti;Apirat Siritaratiwat;Pattasad Seangwong","doi":"10.1109/OJIES.2026.3699362","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3699362","url":null,"abstract":"Accurate fault diagnosis of electrical machines is essential for operational reliability and safety. Handcrafted features remain attractive in practice because of their interpretability and computational efficiency. However, this traditional approach relies on individual feature vectors and is limited in preserving inherent temporal dynamics. To address this issue, a label-consistent input structuring based on the second-stage sliding window (SSSW) method is proposed. This approach retains the benefits of handcrafted features and arranges feature vectors into temporally coherent sequences while ensuring label consistency. A hyperparameter optimization scheme is incorporated with a long short-term memory classifier to reduce manual tuning. Performance evaluations demonstrate the robustness of the proposed SSSW method across diverse operating conditions and input signal configurations. These include single-phase currents, multiphase currents, vibration, and fused current-vibration signals. Notably, the proposed approach achieves classification accuracy of up to 100% and demonstrates stable learning behavior. Experimental verification on a laboratory-scale permanent magnet synchronous motor testbed further validates the proposed method under realistic measurement noise and interference conditions. Finally, combining handcrafted features with the proposed SSSW input structuring method provides a practical, scalable solution for reliable electrical machine fault diagnosis.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"964-976"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11544091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Open Journal of the Industrial Electronics Society Instructions for Authors","authors":"","doi":"10.1109/OJIES.2025.3646952","DOIUrl":"https://doi.org/10.1109/OJIES.2025.3646952","url":null,"abstract":"","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"C4-C4"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11456920","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Achieving EMC Compliance in Traction Inverters via Multiobjective Optimization","authors":"Patrick Dominik Gsoels;Jose Romero Lopera;Herbert Hackl;Christian Manfred Riener;Franz Vollmaier;Martin Stoiber;Jan Henninger;Markus Kratzer;Martin Heinisch;Bernhard Auinger;Jan Carsten Hansen;Bernd Deutschmann","doi":"10.1109/OJIES.2026.3673213","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3673213","url":null,"abstract":"Achieving first-time-right electromagnetic compatibility (EMC) is crucial in automotive industry for competitiveness. However, compliance with EM emission limits is usually evaluated through measurements on hardware prototypes, especially for complex system like electric vehicle powertrains, which include multiple components such as traction inverters that collectively contribute to the overall emissions. Furthermore, several design goals, such as the amount of magnetic material or total installed filter capacitances, must be considered. Therefore, this article introduces a simulation framework based on common- and differential-mode emission surrogate models with 16 design parameters. The models are derived from a circuit simulation model valid up to <inline-formula><tex-math>$30 ,mathrm{M}mathrm{Hz}$</tex-math></inline-formula> and enable the prediction and optimization of conducted emissions (CEs) of traction inverters in the concept phase, prior to hardware prototyping. A Two-Level Traction Inverter system and CISPR 25 standard limits serve as an example. According to the feature selective validation metric, the surrogate models achieve a worst-case accuracy rated as “very good” for CE prediction when trained on a dataset of 8064 samples. This corresponds to a spectral uncertainty of approximately <inline-formula><tex-math>$pm 2 ,mathrm{dB}$</tex-math></inline-formula>. With evaluation times of about <inline-formula><tex-math>$15 ,mathrm{m}mathrm{s}$</tex-math></inline-formula>, the presented surrogate models allow to perform 100 million TI optimizations in just <inline-formula><tex-math>$48 ,mathrm{h}$</tex-math></inline-formula>, resulting in three selected optimal designs verified by circuit simulations. Therefore, the framework acts as a potential tool to develop first-time-right EMC and performance optimized prototypes.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"525-550"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11431113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of IEEE Std. 1459-2010 and Hjorth Descriptors for the Identification of Stator Faults in Induction Motors","authors":"Arthur C. Santos;Marcelo F. Castoldi;Wesley A. Souza;Alessandro Goedtel","doi":"10.1109/OJIES.2026.3686717","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3686717","url":null,"abstract":"Stator interturn short-circuit (ITSC) faults represent a significant fraction of failures in three-phase induction motors (TIMs), with industrial studies reporting occurrences of up to 36%. Current-based diagnostic techniques are widely employed; however, their effectiveness degrades under inherent supply asymmetries, manufacturing tolerances, and moderate voltage imbalances, which introduce negative-sequence and harmonic components that mask incipient fault signatures and can produce current components similar to those generated by internal electromagnetic asymmetries. Diagnostic capability is investigated using two families of features extracted from stator electrical signals: power quantities defined by IEEE Std. 1459-2010 and Hjorth time-domain descriptors. These feature families correspond to distinct physical manifestations observable in the measured currents: macroscopic redistribution of electrical power under nonsinusoidal and unbalanced operating conditions, and local variations in waveform geometry associated with circulating currents and early flux asymmetries. Model performance is evaluated through stratified <inline-formula><tex-math>$k$</tex-math></inline-formula>-fold cross-validation using five supervised classifiers applied to a labeled laboratory dataset comprising multiple ITSC severities (healthy, 1%, 3%, 5%, and 10%), controlled voltage unbalance, multiple mechanical loading conditions, and supply-frequency variation. Hjorth parameters present higher sensitivity to low-severity faults and achieve classification accuracies above 99%, whereas IEEE Std. 1459-2010 quantities yield accuracies between 78% and 95%, reflecting reduced sensitivity to weak distortions but intrinsically phase-invariant behavior. A reduced hybrid feature set combining selected IEEE Std. 1459-2010 descriptors with the Hjorth parameters activity and mobility reduces phase dependence and produces classification accuracies exceeding 99.5% across all evaluated classifiers. Correlation analysis and feature-importance rankings indicate limited redundancy between descriptor groups and reveal complementary diagnostic information associated with electrical power redistribution and waveform-geometry variations. The obtained results indicate that early-stage ITSC faults are identifiable using a compact descriptor space derived from physically interpretable electrical quantities, without dependence on high-dimensional time-frequency representations or deep learning models.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"825-852"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11491852","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid Classical Quantum Framework for Electromagnetic Interference Analysis and Optimization in Network Cables","authors":"Abbas Seif;Hamid Radmanesh;Amir Ahmarinejad;Ahmad Ali Ashrafian","doi":"10.1109/OJIES.2026.3663764","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3663764","url":null,"abstract":"This article proposes a quantum-assisted optimization framework for mitigating probe-induced electromagnetic interference (EMI) in wired network cables. An experimentally grounded electromagnetic model is coupled with a discretized formulation of the Quantum Approximate Optimization Algorithm (QAOA), enabling structured parameter tuning under realistic constraints. The optimization problem is implemented using a three-qubit QAOA and evaluated on a statevector simulator, consistent with current noisy intermediate-scale quantum limitations. Simulation results, aligned with experimental EMI models, show that the quantum-assisted approach can outperform a classical particle swarm optimization baseline in selected high-interference scenarios, achieving approximately one order of magnitude improvement in bit error rate for specific parameter regimes. These improvements are problem-dependent and do not represent a general quantum advantage. The proposed formulation establishes a clear correspondence between physical EMI parameters and quantum optimization variables, highlighting the potential of quantum-assisted optimization as a complementary tool for EMI mitigation and motivating further investigation of noise-aware and experimentally integrated quantum strategies.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"1068-1079"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11393472","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148488339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances and Challenges for Magnetic Planarization and Integration – An Overview","authors":"Yue Liu;Hongfei Wu;Chaoqiang Jiang;Xufu Ren;Shaoliang An;Teng Long","doi":"10.1109/OJIES.2026.3679790","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3679790","url":null,"abstract":"Efficiency, power density, and switching frequency are the three key drivers for advancing high-performance power supplies. However, the reliance on conventional wire-wound magnetic components has significantly hindered the development of this field. This article provides a comprehensive overview of magnetic planarization and integration technologies, which are increasingly adopted in modern power electronic converters to enhance efficiency and power density. It elaborates on the methodologies for planarizing magnetic components, including the impacts and implementations of transformer and inductor integration. In addition, printed circuit board winding strategies are discussed, focusing on current sharing techniques, fractional turn structures, and integrated network design. The effects of air gaps during planarization are also analyzed in detail. This overview aims to help power electronics engineers and researchers gain a clear understanding of recent progress in magnetic planarization and integration technologies and the prevailing challenges in this field.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"747-765"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11460210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148491233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}