Reliability Analysis of High-Voltage Drive Motor Systems in Terms of the Polymorphic Bayesian Network

Weiguang Zheng, Haonan Jiang (), Shande Li () and Qiuxiang Ma
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Weiguang Zheng: School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545616, China
Haonan Jiang: School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
Shande Li: State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Qiuxiang Ma: Commercial Vehicle Technology Center, Dong Feng Liuzhou Automobile Co., Ltd., Liuzhou 545005, China

Mathematics, 2023, vol. 11, issue 10, 1-21

Abstract: The reliability of the high-voltage drive motor system for pure electric commercial vehicles is in premium demand. Conventional reliability based on fault tree analysis methods is not suitable for the quantitative assessment of polymorphic systems. As an example of a pure electric commercial vehicle, this paper combines polymorphic theory and Bayesian theory to establish a polymorphic Bayesian network model of a high-voltage drive motor system in terms of a polymorphic fault tree and to quantitatively judge the system. The polymorphic Bayesian network (BN) model can accurately depict the high-voltage drive motor system’s miscellaneous fault states and solve the top event’s probability in every state, also solving the system and drawing the consistent conclusion that the presence of abrasive particles, high-temperature gluing, moisture, and localized high temperatures are the system’s weak links by solving the critical importance, probabilistic importance, and posterior probability of the underlying event, which provides a theoretical reference for structure contrive optimization and fault diagnosis. This is extremely important in terms of improving pure electric commercial vehicles’ high-voltage drive motor systems.

Keywords: quantitative assessment; polymorphic fault tree; polymorphic Bayesian network; probability importance; critical importance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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