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Novel solution for sequential fault diagnosis based on a growing algorithm

Author

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  • Tian, Heng
  • Duan, Fuhai
  • Fan, Liang
  • Sang, Yong

Abstract

Test sequencing for binary systems is an NP-complete problem. In this study, we introduce a novel algorithm for this problem, which is defined as a growing algorithm. This algorithm chooses the failure states and finds a suitable test set for the selected failure states. This can avoid the backtracking approach of the traditional algorithms. Three main procedures are presented to illustrate the growing algorithm: (1) a test sequencing problem is simplified as a combinatorial problem comprising a basic test set with unnecessary tests; (2) the optimal test sequence generating algorithm (OTSGA) is proposed for an individual failure state; and (3) the priority levels of the failure states are determined based on their prior probabilities. Finally, a circuit system is used to show how the growing algorithm works, and five real-word D matrices are employed to validate the universality and stability of the algorithm. Subsequently, the application scope for the growing algorithm is demonstrated in detail by stochastic simulation experiments. This growing algorithm is suitable for large-scale systems with a sparse D matrix, and it obtains good calculation results with a short running time and high efficiency.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:192:y:2019:i:c:s095183201830022x
    DOI: 10.1016/j.ress.2018.06.002
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    References listed on IDEAS

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    1. Sen Deng & Bo Jing & Hongliang Zhou, 2017. "Heuristic particle swarm optimization approach for test point selection with imperfect test," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 37-50, January.
    2. 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.
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    Cited by:

    1. 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).

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