IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04103914.html
   My bibliography  Save this paper

Importance measures for critical components in complex system based on Copula Hierarchical Bayesian Network

Author

Listed:
  • Rentong Chen

    (BUAA - Beihang University, POLIMI - Politecnico di Milano [Milan])

  • Chao Zhang

    (BUAA - Beihang University)

  • Shaoping Wang

    (BUAA - Beihang University)

  • Enrico Zio

    (POLIMI - Politecnico di Milano [Milan], CRC - Centre de recherche sur les Risques et les Crises - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres)

  • Hongyan Dui

    (Zhengzhou University)

  • Yadong Zhang

    (BUAA - Beihang University)

Abstract

In order to identify the vulnerable components and ensure the required reliability of mechatronics systems, importance measures of critical components are crucially used in the early design of systems. However, complex mechatronics systems have the properties of hierarchy, nonlinearity, dependency, uncertainty, and randomness, which make it difficult to analyze the coupling failure mechanisms, model the system, estimate its reliability, and complete importance measures of its components. This paper proposes importance measures for components with continuous time degradation. The Wiener process model is used to describe the continuous-time degradation process, and the Copula Hierarchical Bayesian Network (CHBN) is developed for system reliability estimation. Six importance measures are proposed for continuous-time degrading components. These importance measures provide a time-dependent analysis of the criticality of components, thus adding insights on the contributions of the components on the system reliability or performance over time. A case study on the harmonic gear drive is then conducted to demonstrate the use of the proposed importance measures. The results of the study show that the CHBN-based importance measures can be a valuable decision-support tool for designers in the early design of systems.

Suggested Citation

  • Rentong Chen & Chao Zhang & Shaoping Wang & Enrico Zio & Hongyan Dui & Yadong Zhang, 2023. "Importance measures for critical components in complex system based on Copula Hierarchical Bayesian Network," Post-Print hal-04103914, HAL.
    • Handle: RePEc:hal:journl:hal-04103914
    DOI: 10.1016/j.ress.2022.108883
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu, Yaocheng & Shuai, Bin & Huang, Wencheng, 2024. "Resilience evaluation of train control on-board system considering common cause failure: Based on a beta-factor and continuous-time bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    2. Cao, Yingsai & Lu, Chen & Dong, Wenjie, 2024. "Importance measures for multi-state systems with multiple components under hierarchical dependences," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    3. Chen, Zhiwei & Zhang, Hao & Wang, Xinyue & Yang, Jinling & Dui, Hongyan, 2024. "Reliability analysis and redundancy design of satellite communication system based on a novel Bayesian environmental importance," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. Yu, Yaocheng & Shuai, Bin & Huang, Wencheng, 2024. "Resilience evaluation of train control on-board system based on multi-dimensional continuous-time Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    5. Zheng, Shuwen & Wang, Chong & Zio, Enrico & Liu, Jie, 2024. "Fault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    6. Wei, Wei & Hu, Qiuyuan & Zhang, Qinghui, 2024. "Improving node connectivity by optimized dual tree-based effective node consolidation," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Mendoza-Lugo, Miguel Angel & Morales-Nápoles, Oswaldo, 2024. "Mapping hazardous locations on a road network due to extreme gross vehicle weights," Reliability Engineering and System Safety, Elsevier, vol. 242(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:hal:journl:hal-04103914. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.