Lecture "Artificial Intelligence-Assisted Design and Fault Diagnosis of Electric Motors for Green Transportation"

#artificial-intelligence #electric-motors #electric-vehicles #fault-detection #green-transportation #deep-learning
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Discover how AI is transforming electric motor design and diagnostics for green transportation. This talk explores the use of machine learning and deep learning to detect faults like inter-turn short-circuits, demagnetization, and bearing issues, enhancing safety and reliability. It also covers AI-driven innovations in motor design, such as noise reduction, highlighting the future of intelligent, sustainable, and efficient transportation systems.

The impact of artificial intelligence (AI) is rapidly growing and is increasingly pivotal across a wide range of disciplines, from innovative scientific research to practical, everyday applications. The powerful capabilities of AI—spanning data analysis, predictive modeling, and beyond—equip researchers and professionals with unparalleled tools to tackle complex problems, push the boundaries of scientific discovery, and elevate productivity to unprecedented levels. This talk will explore the integration of AI in diagnosing motor faults and advancing motor design, highlighting how AI can significantly enhance the reliability and performance of electric motors in green transportation. It will delve into the use of machine learning and deep learning models to predict and prevent motor failures (e.g., inter-turn short-circuits, demagnetization, and bearing faults) [1]-[3], which is essential for ensuring safety and reliability in transportation and industry. Furthermore, the talk will highlight AI-driven innovations in motor design [4], such as noise-reduction, offering insights into how AI can revolutionize traditional motor systems and contribute to ongoing improvements in predictive maintenance and design practices.

[1]  A. Mohammad-Alikhani, B. Nahid-Mobarakeh, and M. F. Hsieh, “One-Dimensional LSTM-Regulated Deep Residual Network for Data-Driven Fault Detection in Electric Machines,” IEEE Trans. Industrial Elect. vol. 71, no. 3, pp. 3083-3092, Mar 2024.
[2]  A. Mohammad-Alikhani, B. Nahid-Mobarakeh, and M. F. Hsieh, “Diagnosis of Mechanical and Electrical Faults in Electric Machines Using a Lightweight Frequency-Scaled Convolutional Neural Network,” IEEE Trans. Energy Conver., early access, Nov 2024, doi: 10.1109/TEC.2024.3490736.
[3]  K. J. Shih, M. F. Hsieh, B. J. Chen, and S. F. Huang, “Machine Learning for Inter-Turn Short-Circuit Fault Diagnosis in Permanent Magnet Synchronous Motors,” IEEE Trans. Magn., vol. 58, no. 8, 8204307, Apr 2022.
[4]  M. F. Hsieh, L. H. Lin, T. A. Huynh, and D. Dorrell, “Development of Machine Learning-Based Design Platform for Permanent Magnet Synchronous Motor Toward Simulation Free,” IEEE Trans. Magn., vol. 59, no. 11, 8204307, Aug 2023.


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  • Plytinės str. 25
  • Vilnius, Lithuania
  • Lithuania
  • Building: P2
  • Room Number: 153
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  • Co-sponsored by VILNIUS TECH Faculty of Electronics
  • Starts 11 July 2025 09:00 PM UTC
  • Ends 03 September 2025 09:00 PM UTC
  • No Admission Charge

  Speakers

Min-Fu Hsieh of National Chen Kung University

Biography:

Min-Fu Hsieh (IEEE M’02–SM’11) received the B.Eng. degree in mechanical engineering from National Cheng Kung University (NCKU), Tainan, Taiwan, in 1991, followed by the M.Sc. and Ph.D. degrees in mechanical engineering from the University of Liverpool, U.K., in 1996 and 2000. From 2000 to 2003, he was a researcher at the Electric Motor Technology Research Center, NCKU. In 2003, he joined the Department of Systems and Naval Mechatronic Engineering at NCKU as an Assistant Professor and was promoted to Full Professor in 2012. Since 2017, Prof. Hsieh has been with NCKU’s Department of Electrical Engineering, where he became a Distinguished Professor in 2022. He has served as the Publication Co-Chair and Guest Editor-in-Chief for several IEEE Intermag conferences. Prof. Hsieh is an Editor for IEEE Transactions on Magnetics and an Associate Editor for IEEE Transactions on Industry Applications. He currently holds roles as International Relations Coordinator and member of the Technical Committee of the IEEE Magnetics Society. His research interests include electric machine design, drive systems, and mechatronics.

IEEE Magnetics Society Position History:
IEEE Magnetics Society Recognitions: