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Risk-based fault detection using Self-Organizing Map

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  • Yu, Hongyang
  • Khan, Faisal
  • Garaniya, Vikram

Abstract

The complexity of modern systems is increasing rapidly and the dominating relationships among system variables have become highly non-linear. This results in difficulty in the identification of a system׳s operating states. In turn, this difficulty affects the sensitivity of fault detection and imposes a challenge on ensuring the safety of operation. In recent years, Self-Organizing Maps has gained popularity in system monitoring as a robust non-linear dimensionality reduction tool. Self-Organizing Map is able to capture non-linear variations of the system. Therefore, it is sensitive to the change of a system׳s states leading to early detection of fault. In this paper, a new approach based on Self-Organizing Map is proposed to detect and assess the risk of fault. In addition, probabilistic analysis is applied to characterize the risk of fault into different levels according to the hazard potential to enable a refined monitoring of the system. The proposed approach is applied on two experimental systems. The results from both systems have shown high sensitivity of the proposed approach in detecting and identifying the root cause of faults. The refined monitoring facilitates the determination of the risk of fault and early deployment of remedial actions and safety measures to minimize the potential impact of fault.

Suggested Citation

  • Yu, Hongyang & Khan, Faisal & Garaniya, Vikram, 2015. "Risk-based fault detection using Self-Organizing Map," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 82-96.
    • Handle: RePEc:eee:reensy:v:139:y:2015:i:c:p:82-96
    DOI: 10.1016/j.ress.2015.02.011
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    Cited by:

    1. Hou, Lei & Wu, Xingguang & Wu, Zhuang & Wu, Shouzhi, 2020. "Pattern identification and risk prediction of domino effect based on data mining methods for accidents occurred in the tank farm," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    3. Rebello, Sinda & Yu, Hongyang & Ma, Lin, 2019. "An integrated approach for real-time hazard mitigation in complex industrial processes," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 297-309.
    4. Kim, Hyeonmin & Kim, Jung Taek & Heo, Gyunyoung, 2018. "Failure rate updates using condition-based prognostics in probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 225-233.

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