TY - GEN
T1 - Enabling data-driven condition monitoring of power electronic systems with artificial intelligence
T2 - Concepts, tools, and developments
AU - Zhao, Shuai
AU - Wang, Huai
N1 - Funding Information:
Shuai Zhao ([email protected]) received the BE (Hons), ME, and Ph.D. degrees in information and communication engineering from Northwestern Polytechnical University, Xi’an, China, in 2011, 2014, and 2018, respectively. He is currently a postdoctoral researcher with the Center of Reliable Power Electronics (CORPE), Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark. From September 2014 to September 2016, he was a visiting Ph.D. student with the Department of Mechanical and Industrial Engineering at the University of Toronto, Toronto, ON, Canada, with a scholarship from China Scholarship Council (CSC). In August 2018, he was a visiting scholar with the Power Electronics and Drives Laboratory, Department of Electrical and Computer Science at the University of Texas at Dallas, Richardson, TX, United States. His research interests include system informatics, intelligent condition monitoring, diagnostics & prognostics, and tailored AI tools for power electronic systems.
Funding Information:
Huai Wang ([email protected]) received BE degree in electrical engineering from Huazhong University of Science and Technology, Wuhan, China, in 2007 and Ph.D. degree in power electronics, from the City University of Hong Kong, Hong Kong, in 2012. He is currently Professor with the Center of Reliable Power Electronics (CORPE), Department of Energy Technology at Aalborg University, 9220 Aalborg, Denmark. He was a visiting scientist with the ETH Zurich, Switzerland, from August to September 2014, and with the Massachusetts Institute of Technology, United States, from September to November 2013. He was with the ABB Corporate Research Center, Switzerland, in 2009. His research addresses the fundamental challenges in modeling and validating power electronic component failure mechanisms and application issues in system-level predictability, condition monitoring, circuit architecture, and robustness design. He leads a project on Light-AI for Cognitive Power Electronics. Dr. Wang received the Richard M. Bass Outstanding Young Power Electronics Engineer Award from the IEEE Power Electronics Society in 2016, and the Green Talents Award from the German Federal Ministry of Education and Research in 2014. He is currently the Chair of the IEEE PELS/IAS/IES Chapter in Denmark. He serves as an associate editor of IEEE Journal of Emerging and Selected Topics in Power Electronics and IEEE Transactions on Power Electronics.
Publisher Copyright:
© 2014 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - Condition monitoring is a proactive measure to realize operation optimization, predictive maintenance, and high availability of Power Electronic Systems (PES). It is demanded by reliability-, safety-, or availability-critical applications. The core of condition monitoring is a prediction based on historical and present information. Artificial Intelligence (AI) could play a role in addressing optimization, regression, and classification problems in predicting the operation or health status of PES. Besides AI algorithms, quality data collection, objective formulation, and result validation require an in-depth understanding of the PES. The nexus between PES and AI expects to create overarching effects in the condition monitoring area. This article presents exploratory efforts in the data-driven condition monitoring of PES in the view of existing challenges and emerging opportunities.
AB - Condition monitoring is a proactive measure to realize operation optimization, predictive maintenance, and high availability of Power Electronic Systems (PES). It is demanded by reliability-, safety-, or availability-critical applications. The core of condition monitoring is a prediction based on historical and present information. Artificial Intelligence (AI) could play a role in addressing optimization, regression, and classification problems in predicting the operation or health status of PES. Besides AI algorithms, quality data collection, objective formulation, and result validation require an in-depth understanding of the PES. The nexus between PES and AI expects to create overarching effects in the condition monitoring area. This article presents exploratory efforts in the data-driven condition monitoring of PES in the view of existing challenges and emerging opportunities.
UR - http://www.scopus.com/inward/record.url?scp=85101859021&partnerID=8YFLogxK
U2 - 10.1109/MPEL.2020.3047718
DO - 10.1109/MPEL.2020.3047718
M3 - Article
AN - SCOPUS:85101859021
SN - 2329-9207
VL - 8
SP - 18
EP - 27
JO - IEEE Power Electronics Magazine
JF - IEEE Power Electronics Magazine
ER -