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An analytical model of electronic fault diagnosis on extension of the dependency theory

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  • Cui, Yiqian
  • Shi, Junyou
  • Wang, Zili

Abstract

Based on the D-matrix model, the dependency theory is widely used in the field of fault diagnosis to model the fault flows in complex electronic systems. However, the traditional dependency model can only handle a single fault; it fails to recognize and diagnose multiple faults. In addition, it is not tolerant with system structural or functional changes. These inherent weaknesses of the traditional dependency theory may lead to unsatisfactory acquisition of the diagnosis results. To solve the problem, an improved dependency model is invented as novel analytic diagnosis model to better describe the relationships between faults and tests. The system fault diagnosis based on the improved dependency model is formulated as an optimization problem with binary logic operations where all the fault hypotheses are tested. The calculation process consists of three steps: establishment of the objective function, determination of the nominal states, and determination of the expected states. Finally, the proposed method is demonstrated via an avionic processor case using the improved dependency model. The optimization-based fault diagnosis problem is formulated and the optimal solution is obtained. The diagnosis result demonstrates that the proposed method is successful on performance assessment and fault diagnosis.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:reensy:v:133:y:2015:i:c:p:192-202
    DOI: 10.1016/j.ress.2014.09.015
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    References listed on IDEAS

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    1. Kwang Pil, Chang & Rausand, Marvin & Vatn, Jørn, 2008. "Reliability assessment of reliquefaction systems on LNG carriers," Reliability Engineering and System Safety, Elsevier, vol. 93(9), pages 1345-1353.
    2. Xing, Liudong & Levitin, Gregory, 2010. "Combinatorial analysis of systems with competing failures subject to failure isolation and propagation effects," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1210-1215.
    3. Wang, Chaonan & Xing, Liudong & Levitin, Gregory, 2013. "Reliability analysis of multi-trigger binary systems subject to competing failures," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 9-17.
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    Cited by:

    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. 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).
    4. 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).
    5. 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.

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