A reliability prediction method considering degradation self-acceleration effect in DC-link electrolytic capacitor

Abstract

AbstractThe reliability of DC-link electrolytic capacitors is crucial to ensure the quality of power supply systems. The degradation of capacitor parameters may lead to a higher temperature and thus accelerate degradation as a self-accelerating effect. In this article, an improved reliability prediction method for DC-link electrolytic capacitors is proposed, as existing methods have not adequately accounted for the self-acceleration effect. The degradation under dynamic stress is obtained by cumulative computations and the stress is updated according to the degraded parameters. The degradation models are converted into degradation rate models to overcome the computational challenges associated with small-step iterations that may make traditional methods unaffordable. The proposed method for developing the degradation rate model is widely applicable and achieves satisfactory accuracy. To demonstrate the practicality of the proposed method, a case study of a boost motor drive system is presented. The appropriate iteration step can be determined by comparing the results of the lifetime distributions obtained using different iteration steps. Degradation paths considering self-acceleration effects can be obtained, enabling more precise system quality analysis and reliability prediction.Keywords: degradation analysiselectrolytic capacitorreliability predictionself-acceleration effect Additional informationNotes on contributorsXuerong YeXuerong Ye is currently a Professor at the Department of Electrical Engineering, Harbin Institute of Technology. He received B.S., M.S., and Ph.D. degrees in electrical engineering from the Harbin Institute of Technology, China. His research interests include robust parameter design and reliability prediction.Qisen SunQisen Sun is a Ph.D. student in the Department of Systems Engineering at the City University of Hong Kong, Hong Kong, China. He received an M.S. degree in electrical engineering from the Harbin Institute of Technology, Harbin, China. His research interests include statistical engineering and reliability prediction.Ruishi LinRuishi Lin is the Deputy Chief Engineer of the Beijing Institute of Aerospace Automation, Beijing, China.Cen ChenCen Chen is currently an Associate Professor at the Department of Electrical Engineering, Harbin Institute of Technology. He received his B.S. and Ph.D. degrees in electrical engineering from the Harbin Institute of Technology, Harbin, China. His research interests include electronic system reliability prediction, fault diagnosis, and health management.Min XieMin Xie is a Chair Professor at the City University of Hong Kong. Before that, he was a Professor at the National University of Singapore. He received his undergraduate and postgraduate education in Sweden with a Ph.D. from Linkoping University in 1987. Prof Xie has published over 300 journal articles and eight books. Prof Xie has been an elected fellow of IEEE since 2006 and has been elected to the European Academy of Sciences and Arts.Guofu ZhaiGuofu Zhai is currently a Professor in the Department of Electrical Engineering at Harbin Institute of Technology. He has published more than 40 peer-reviewed journal articles. His research interests include quality control and process monitoring, robust parameter design, and reliability.Rui KangRui Kang is a professor in School of Reliability and Systems Engineering, Beihang University, Beijing, China. He is currently serving as the associate editor of IEEE Trans. on Reliability and is the founder of China Prognostics and Health Management Society. His main research interests include reliability, resilience for complex system, and modeling epistemic uncertainty in reliability and maintainability.