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A statistical dependent failure detection method for n-component parallel systems

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  • Ota, Shuhei
  • Kimura, Mitsuhiro

Abstract

Making a system redundant by combining identical components is a useful way to ensure a highly reliable system. However, the components of such systems may fail mutually, and if the components break down dependently, the reliability of the system decreases. Therefore, reliability analysis considering the dependence among the components is important in reliability assessment. This research proposes a statistical detection method of the dependent failure occurrence in n-component parallel systems that utilizes the failure occurrence times of the components. If we assume that the lifetime distribution of the components worsens if k-out-of-n components failed, the dependent failure occurrence can be found by identifying the change of the distribution. The performance of the proposed method is demonstrated by simulation studies.

Suggested Citation

  • Ota, Shuhei & Kimura, Mitsuhiro, 2017. "A statistical dependent failure detection method for n-component parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 376-382.
  • Handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:376-382
    DOI: 10.1016/j.ress.2017.06.022
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    References listed on IDEAS

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    1. Zhang, Liangwei & Lin, Jing & Karim, Ramin, 2015. "An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 482-497.
    2. O’Connor, Andrew & Mosleh, Ali, 2016. "A general cause based methodology for analysis of common cause and dependent failures in system risk and reliability assessments," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 341-350.
    3. Zhang, Nan & Fouladirad, Mitra & Barros, Anne, 2017. "Maintenance analysis of a two-component load-sharing system," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 67-74.
    4. Hernandez-Fajardo, Isaac & Dueñas-Osorio, Leonardo, 2013. "Probabilistic study of cascading failures in complex interdependent lifeline systems," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 260-272.
    5. Rocco S., Claudio M. & Zio, Enrico, 2007. "A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 593-600.
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    Cited by:

    1. Eryilmaz, Serkan & Ozkut, Murat, 2020. "Optimization problems for a parallel system with multiple types of dependent components," Reliability Engineering and System Safety, Elsevier, vol. 199(C).

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