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Two fault classification methods for large systems when available data are limited

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  • Kim, Kyungmee O.
  • Zuo, Ming J.

Abstract

In this paper, we consider the problem of fault diagnosis for a system with many possible fault types. Two approaches are presented that are useful for initial diagnosis of system-wide faults, assuming that no data are available before commissioning the system but the possibility of the occurrence of each symptom is known for each fault. The first method uses a fault tree approach to reduce the solution space before applying the geometric classification method, the assumption being that no unwanted symptoms are possible. This method is nonparametric and thus does not require any data to estimate the underlying distribution of faults and symptoms. The second method is based on the Bayes classification approach to utilize the subjective information and the limited data that may be available. The two methods are generic and applicable to a variety of industrial processes.

Suggested Citation

  • Kim, Kyungmee O. & Zuo, Ming J., 2007. "Two fault classification methods for large systems when available data are limited," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 585-592.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:5:p:585-592
    DOI: 10.1016/j.ress.2006.02.001
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    Cited by:

    1. Lu, Lu & Xu, Zhengguo & Wang, Wenhai & Sun, Youxian, 2013. "A new fault detection method for computer networks," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 45-51.
    2. Gómez, M.J. & Castejón, C. & García-Prada, J.C., 2016. "Automatic condition monitoring system for crack detection in rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 239-247.
    3. Enrico Zio & Piero Baraldi & Irina C. Popescu, 2008. "A Fuzzy Decision Tree for Fault Classification," Risk Analysis, John Wiley & Sons, vol. 28(1), pages 49-67, February.
    4. Huang, Keke & Tao, Shijun & Wu, Dehao & Yang, Chunhua & Gui, Weihua, 2024. "Robust condition identification against label noise in industrial processes based on trusted connection dictionary learning," Reliability Engineering and System Safety, Elsevier, vol. 247(C).

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