IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v234y2020i6p779-792.html
   My bibliography  Save this article

Novel algorithms for sequential fault diagnosis based on greedy method

Author

Listed:
  • Heng Tian
  • Fuhai Duan
  • Yong Sang
  • Liang Fan

Abstract

Test sequencing for binary systems is a nondeterministic polynomial-complete problem, where greedy algorithms have been proposed to find the solution. The traditional greedy algorithms only extract a single kind of information from the D -matrix to search the optimal test sequence, so their application scope is limited. In this study, two novel greedy algorithms that combine the weight index for fault detection with the information entropy are introduced for this problem, which are defined as the Mix 1 algorithm and the Mix 2 algorithm. First, the application scope for the traditional greedy algorithms is demonstrated in detail by stochastic simulation experiments. Second, two new heuristic formulas are presented, and their scale factors are determined. Third, an example is used to show how the two new algorithms work, and four real-world D- matrices are employed to validate their universality and stability. Finally, the application scope of the Mix 1 and Mix 2 algorithms is determined based on stochastic simulation experiments, and the two greedy algorithms are also used to improve a multistep look-ahead heuristic algorithm. The Mix 1 and Mix 2 algorithms can obtain good results in a reasonable time and have a wide application scope, which also can be used to improve the multistep look-ahead heuristic algorithm.

Suggested Citation

  • Heng Tian & Fuhai Duan & Yong Sang & Liang Fan, 2020. "Novel algorithms for sequential fault diagnosis based on greedy method," Journal of Risk and Reliability, , vol. 234(6), pages 779-792, December.
  • Handle: RePEc:sae:risrel:v:234:y:2020:i:6:p:779-792
    DOI: 10.1177/1748006X20914498
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X20914498
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1748006X20914498?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Cui, Yiqian & Shi, Junyou & Wang, Zili, 2015. "An analytical model of electronic fault diagnosis on extension of the dependency theory," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 192-202.
    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. Xiaofeng Lv & Deyun Zhou & Yongchuan Tang & Ling Ma, 2018. "An Improved Test Selection Optimization Model Based on Fault Ambiguity Group Isolation and Chaotic Discrete PSO," Complexity, Hindawi, vol. 2018, pages 1-10, January.
    2. Tian, Heng & Duan, Fuhai & Fan, Liang & Sang, Yong, 2019. "Novel solution for sequential fault diagnosis based on a growing algorithm," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    3. Shi, Junyou & He, Qingjie & Wang, Zili, 2020. "Integrated Stateflow-based simulation modelling and testability evaluation for electronic built-in-test (BIT) systems," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    4. Wang, Jingyuan & Liu, Zhen & Wang, Jiahong & Long, Bing & Zhou, Xiuyun, 2022. "A general enhancement method for test strategy generation for the sequential fault diagnosis of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 228(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:sae:risrel:v:234:y:2020:i:6:p:779-792. 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: SAGE Publications (email available below). General contact details of provider: .

    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.