PRESENTATION
Artificial Intelligence-assisted Predictive Maintenance for Power Electronics
As power electronics advance into data-rich systems, artificial intelligence offers significant benefits for predictive maintenance applications. This talk will provide an overview of the latest developments in AI-assisted predictive maintenance for power electronics. As a synergistic field that integrates data science and power electronics, it will begin with a systematic introduction to AI-assisted predictive maintenance for power electronic systems. The industrial requirements and specific features of predictive maintenance in power electronics will be examined. The presentation will include case studies on digital twins and condition & health monitoring, featuring emerging AI tools such as data-light AI, computation-light AI, and physics-informed AI. Finally, the discussion will cover open-access resources and new opportunities in this field.
Shuai Zhao
Aalborg University
Shuai Zhao is currently an Assistant Professor with AAU Energy, Aalborg University, Denmark. He received B.S., M.S., and Ph.D. degrees in information and telecommunication engineering from Northwestern Polytechnical University, Xi’an, China, in 2011, 2014, and 2018, respectively. He was a visiting Ph.D. student (2014-2016) at the University of Toronto, Canada, a visiting scholar (2018) with the University of Texas at Dallas, USA, and a Postdoc researcher (2018-2022) with AAU Energy, Aalborg University, Denmark. He is the Associate Editor of IEEE Transactions on Vehicle Technology and Guest Editor of the IEEE Journal of Emerging and Selected Topics in Industrial Electronics. As PI, he leads a European Horizon MSCA-SE project "E-powertrain Predictive Maintenance Using Physics Informed Learning (TEAMING)". His research interests include physics-informed machine learning, system informatics, condition & health monitoring, and tailored AI tools for power electronic systems. Dr. Zhao is an IEEE Senior member.