Article
Engineering, Electrical & Electronic
Zicheng Liu, Lanlan Fang, Dong Jiang, Ronghai Qu
Summary: An improved machine-learning-based fault diagnosis method with adaptive secondary sampling filtering is proposed for multiphase drive systems, which can be applied to different types of multiphase machines. Experimental results validate its satisfying generalization capability as well as high accuracy and strong robustness.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Yang Xia, Yan Xu, Bin Gou, Qingli Deng
Summary: This article proposes a speed sensor fault diagnosis methodology in induction motor drive systems. The method utilizes a learning-based data-driven principle and involves signal estimation, residual evaluation, and decision-making based on outlier test. The speed estimation is achieved through a nonlinear autoregressive exogenous (NARX) learning model and a randomized learning technique called random vector functional link (RVFL) network. The proposed approach shows promising performance in offline and real-time tests, without requiring motor parameters or additional hardware.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Multidisciplinary
Yecheng Zhang
Summary: This paper proposes a fault diagnostic method for OC faults in closed-loop controlled PMSM drive systems based on current behavior. The method can rapidly and accurately locate the fault position and has good robustness.
Article
Engineering, Mechanical
Gang Yu, Mang Gao, Chengli Jia
Summary: In rotation machinery fault diagnosis, useful fault features are often overshadowed by noise and other interference factors. This study proposes a fast algorithm based on an adaptive impulsive wavelet to denoise vibration signals and extract fault characteristic frequency by identifying modeling parameters.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Chao Yang, Zhiliang Wu, Tao Peng, Hongqiu Zhu, Chunhua Yang
Summary: In this article, a new method for transient fault diagnosis in high-speed train TDCS is proposed, which can handle TF scenarios with small amplitude, short duration, and energy feature pattern. The research overcomes the ambiguity between transient signals and noise through parameter optimization and energy distribution.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Article
Automation & Control Systems
Xuefeng Jiang, Jingyu Zhou, Kaiwen Wang, Shirui Yang, Zhijian Wei, Jiazheng Liu
Summary: This article proposes a fault diagnosis strategy based on fuzzy logic for diagnosing different types of open-circuit faults in a novel dual-winding fault-tolerant permanent magnet motor drive. The strategy demonstrates strong robustness and accuracy, allowing for quick identification and quantification of open-circuit faults in the DFPMM drive, as well as providing appropriate remedial measures.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Computer Science, Information Systems
Fazel Mohammadi, Mehrdad Saif
Summary: This article proposes a hybrid fault diagnosis approach to detect and address open-circuit faults in IGBTs used in electric drive-motor systems of hybrid electric vehicles. The method combines model-based and data-driven techniques, utilizing phase voltage analysis and the modified multi-class support vector machine algorithm for accurate and quick fault diagnosis.
Article
Engineering, Mechanical
Shaopeng Zhu, Haojun Li, Guodong Wang, Chenyang Kuang, Huipeng Chen, Jian Gao, Wei Xie
Summary: This paper addresses the fault problem in distributed-four-wheel-drive electric vehicle drive systems. First, a fault-factor-based active fault diagnosis strategy is proposed. Second, a fault-tolerant controller is designed to reconstruct motor drive torque based on vehicle stability. The proposed control strategy accurately diagnoses the operating state of the motor, rebuilds the motor torque based on stability, and demonstrates robust stability when the drive system fails.
Article
Engineering, Electrical & Electronic
Shuyue Guan, Darong Huang, Shenghui Guo, Ling Zhao, Hongtian Chen
Summary: This study proposes a novel approach for fault diagnosis in complex industrial systems by combining particle swarm optimization (PSO) with wavelet mutation and least square support vector machine (LSSVM). The method decomposes and reconstructs signals to extract fault features, and establishes a fault diagnosis model to improve classification accuracy. Experimental results show that the proposed approach outperforms traditional methods in terms of fault recognition efficiency.
Article
Energy & Fuels
Xinyang Zhang, Jichao Hong, Xiaoming Xu
Summary: The entropy algorithm is an efficient fault diagnosis technology with potential in battery safety protection for electric vehicles. This study compares two commonly used entropy algorithms for battery fault diagnosis, focusing on their ability to diagnose abnormal fluctuations in the prefault phase. By exploring the influence of key calculation factors on the diagnostic effect, the study discovers a significant normal distribution pattern and logarithmic relationship between diagnostic effect and calculation window size and scale factor. The findings provide a theoretical basis for improving diagnosis efficiency. The study also proposes a multi-level diagnosis strategy based on statistical methods, which demonstrates strong robustness and generality in actual vehicle fault data verification.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Engineering, Multidisciplinary
Shengbo Wang, Guiming Mei, Bingyan Chen, Yao Cheng, Bin Cheng
Summary: This study proposes an enhanced optimal gradient product filtering (EOGPF) method to improve the extraction performance of fault-associated features in bearing fault diagnosis. By optimizing morphological operators and developing an optimal gradient product operator, the EOGPF method effectively extracts fault-induced impulse features and combines noise removal and feature extraction techniques for bearing fault diagnosis.
Article
Automation & Control Systems
Yitao Cheng, Yao Sun, Xing Li, Hanbing Dan, Jianheng Lin, Mei Su
Summary: A robust open-switch fault-diagnosis method based on the common-mode voltages of the inverter in IM-drive systems is proposed in this study, utilizing the characteristics of common-mode voltages for fault detection and localization. An active common-mode voltage injection method is introduced to improve diagnosis results credibility and reduce missed detection rate. The method only uses measured current information, avoiding the application of extra sensors.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Engineering, Electrical & Electronic
Ze Yang, Jie Ma, Ruihang Ji, Baoqing Yang, Xuwei Fan
Summary: This work investigates the actuator fault detection, isolation, and reconstruction for the spacecraft attitude control system under unknown model uncertainty and colored measurement noise. A novel intelligent adaptive robust strong tracking square root cubature Kalman filter is proposed for parameter estimation. Observers are designed based on the filter to estimate disturbance and fault, and a dual-observer FDIR scheme is proposed with the introduction of a gated recurrent unit neural network for better robustness.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Electrical & Electronic
Michael Schmid, Emanuel Gebauer, Christian Hanzl, Christian Endisch
Summary: This article develops and validates a model-based fault diagnosis algorithm that utilizes the switches of an RBS to improve fault isolability, utilizes a fuzzy clustering approach for fault isolation, and enhances sensitivity and robustness of the fault diagnosis method with a constrained sigma-point Kalman filter.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2021)
Article
Automation & Control Systems
Constantinos Heracleous, Christodoulos Keliris, Christos G. Panayiotou, Marios M. Polycarpou
Summary: This paper presents a fault diagnosis architecture for a class of hybrid systems with nonlinear uncertain dynamics, measurement noise, and mode transitions. The proposed approach utilizes a hybrid estimator based on a modified automaton framework, a filtering approach for fault detection, and an autonomous guard events identification scheme. Additionally, it includes a fault isolation scheme that anticipates potential fault events and employs suitable isolation estimators. Simulation results demonstrate the effectiveness of the proposed approach.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Weiqiang Chen, Lingyi Zhang, Pattipati Krishna, Ali M. Bazzi, Shailesh Joshi, Ercan M. Dede
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2020)
Article
Engineering, Electrical & Electronic
Weiqiang Chen, Ali Bazzi
Summary: This article introduces a model-based voltage quality analysis and optimization method for post-fault reconfigured N-level Neutral Point Clamped (NPC) inverter, aiming to address the significant degradation of voltage quality in post-fault reconfigured multilevel inverters (MLIs) which may impact critical applications. By reviewing existing voltage quality and mathematical analyses of MLIs, a suitable mathematical model for N-level NPC inverter is proposed in order to accurately describe its voltage quality. An optimization method is also introduced based on the accurate mathematical model to alleviate the voltage quality degradation issue in post-fault reconfigured N-level NPC inverter.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Muhammed Ali Gultekin, Zhe Zhang, Ali Bazzi
Summary: This study utilizes a data-driven method, DMDc, to model faulty behavior of an inverter-fed induction machine. Results demonstrate the algorithm's ability to accurately predict system behavior, independent of system parameters. The model shows promise for data-driven fault diagnostics and system modeling.
2021 IEEE INTERNATIONAL ELECTRIC MACHINES & DRIVES CONFERENCE (IEMDC)
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Muhammed Ali Gultekin, Qian Yang, Ali Bazzi, Krishna Pattipati, Shailesh Joshi, Muhamed Farooq, Hiroshi Ukegawa
Summary: This paper investigates the degradation of on-state resistance and gate-to-source capacitance in Si MOSFETs using two different test methods. The results show that degradation occurs regardless of the method used, with a gradual increase in impedance over time.
2021 IEEE INTERNATIONAL ELECTRIC MACHINES & DRIVES CONFERENCE (IEMDC)
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Abbas Hassan, Ali Bazzi
Summary: This paper discusses an adaptive loss minimization technique for induction motor drive systems, using an extended Kalman filter to estimate the torque and rotor speed values required to minimize the overall system loss. Additionally, the effects of parameter uncertainty on the performance of the loss minimization technique are analyzed.
2021 IEEE INTERNATIONAL ELECTRIC MACHINES & DRIVES CONFERENCE (IEMDC)
(2021)
Article
Computer Science, Information Systems
Hiep Hoang Nguyen, Arshiah Y. Mirza, Weiqiang Chen, Yiqi Liu, Joanne Ronzello, Jack Chapman, Ali M. Bazzi, Yang Cao
Summary: This paper presents the development and performance evaluation of revolutionary nanostructured insulation for large propulsion motors. The nanostructured insulation based on 2D-platelet fillers offers significant improvements in electrical, dielectric, thermal, and mechanical properties compared to conventional insulation systems. Studies show a remarkable increase in torque handling capacity of motors using the proposed nanostructured insulation material.
Proceedings Paper
Energy & Fuels
Vahe Seferian, Ali Bazzi, Hazem Hajj
2020 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE)
(2020)
Proceedings Paper
Energy & Fuels
Zhe Zhang, Ali M. Bazzi
2020 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE)
(2020)
Proceedings Paper
Energy & Fuels
Abbas Hassan, Ali Bazzi
2020 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE)
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
Brian R. Engelhart, Ali M. Bazzi
2020 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE & EXPO (ITEC)
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
Abbas Hassan, Hani Sadek, Ali Bazzi, Naseem Daher
2020 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE & EXPO (ITEC)
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
Jonathan Blake, Weiqiang Chen, Ali Bazzi
2020 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE & EXPO (ITEC)
(2020)
Proceedings Paper
Energy & Fuels
Zhe Zhang, Ali M. Bazzi, Afia Semin
2020 THIRTY-FIFTH ANNUAL IEEE APPLIED POWER ELECTRONICS CONFERENCE AND EXPOSITION (APEC 2020)
(2020)
Article
Engineering, Multidisciplinary
Zhe Zhang, Lixiang Wei, Peizhong Yi, Yujia Cui, Puneeth Srikanta Murthy, Ali M. Bazzi
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2020)
Proceedings Paper
Engineering, Marine
Arshiah Y. Mirza, Joshua Dupont, Ali M. Bazzi
2019 IEEE ELECTRIC SHIP TECHNOLOGIES SYMPOSIUM (ESTS 2019): EMERGING TECHNOLOGIES FOR FUTURE ELECTRIC SHIPS
(2019)
