Artificial Intelligence-Assisted Design and Fault Diagnosis of Electric Motors for Green Transportation
A talk by Prof. Min-Fu Hsieh of National Cheng Kung University (NCKU), Tainan Taiwan, exploring 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), which is essential for ensuring safety and reliability in transportation and industry. Furthermore, the talk will highlight AI-driven innovations in motor design, 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.
Date and Time
Location
Hosts
Registration
- Date: 28 Jun 2025
- Time: 01:30 AM UTC to 03:00 AM UTC
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- 1120 Ringwood Ct
- San Jose, California
- United States 95131
- Click here for Map
- Starts 16 April 2025 07:00 AM UTC
- Ends 28 June 2025 02:00 AM UTC
- No Admission Charge
Speakers
Design and Fault Diagnosis of Electric Motors
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.
Agenda
| 6:30 - 7:00 | Socializing and Networking at Quadrant |
| 6:55 | Zoom session will be online with Waiting Room |
| 7:00 - 7:45 | Lecture begins, online and in person |
| 7:45 - 8:00 | Questions and Answers |