IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i10p2378-d1151468.html
   My bibliography  Save this article

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

Author

Listed:
  • Weiguang Zheng

    (School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545616, China
    School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China)

  • Haonan Jiang

    (School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
    Commercial Vehicle Technology Center, Dong Feng Liuzhou Automobile Co., Ltd., Liuzhou 545005, 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)

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.

Suggested Citation

  • Weiguang Zheng & Haonan Jiang & Shande Li & Qiuxiang Ma, 2023. "Reliability Analysis of High-Voltage Drive Motor Systems in Terms of the Polymorphic Bayesian Network," Mathematics, MDPI, vol. 11(10), pages 1-21, May.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2378-:d:1151468
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/10/2378/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/10/2378/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lu, Shaoqi & Shi, Daimin & Xiao, Hui, 2019. "Reliability of sliding window systems with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 366-376.
    2. Rui-Jun Zhang & Lu-Lu Zhang & Ming-Xiao Dong, 2015. "Multi-state system importance analysis method of fuzzy Bayesian networks," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 21(3), pages 395-414.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bigatti, A.M. & Pascual-Ortigosa, P. & Sáenz-de-Cabezón, E., 2021. "A C++ class for multi-state algebraic reliability computations," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Wang, Wei & Fang, Chao & Wang, Yan & Li, Jin, 2022. "Reliability Modeling and Optimization of Circular Multi-State Sliding Time Window System with Sequential Demands," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Wu, Congshan & Zhao, Xian & Wang, Siqi & Song, Yanbo, 2022. "Reliability analysis of consecutive-k-out-of-r-from-n subsystems: F balanced systems with load sharing," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    4. Mo, Yuchang & Xing, Liudong & Zhang, Lejun & Cai, Shaobin, 2020. "Performability analysis of multi-state sliding window systems," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    5. Wang, Wei & Fang, Chao & Liu, Shan & Xiang, Yisha, 2021. "Reliability analysis and optimization of multi-state sliding window system with sequential demands and time constraints," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    6. Xiao, Hui & Yi, Kunxiang & Liu, Haitao & Kou, Gang, 2021. "Reliability modeling and optimization of a two-dimensional sliding window system," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    7. Wang, Wei & Fu, Yongnian & Si, Peng & Lin, Mingqiang, 2020. "Reliability analysis of circular multi-state sliding window system with sequential demands," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    8. Ding, Yi & Hu, Yishuang & Li, Daqing, 2021. "Redundancy Optimization for Multi-Performance Multi-State Series-Parallel Systems Considering Reliability Requirements," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    9. Xiao, Hui & Zhang, Yiyun & Xiang, Yisha & Peng, Rui, 2020. "Optimal design of a linear sliding window system with consideration of performance sharing," Reliability Engineering and System Safety, Elsevier, vol. 198(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2378-:d:1151468. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.