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Reliability evaluation and component importance measure for manufacturing systems based on failure losses

Author

Listed:
  • Ding Zhang

    (Xi’an Jiaotong University)

  • Yingjie Zhang

    (Xi’an Jiaotong University)

  • Mingrang Yu

    (Xi’an Jiaotong University)

  • Yun Chen

    (Xi’an Jiaotong University)

Abstract

Little work has been done to assess the reliability of a vital system like the manufacturing system. In this article, a novel and effective system reliability evaluation method in terms of failure losses has been proposed for manufacturing systems of job shop type, and then the failure losses based component importance measure (CIM) is used for importance analysis of equipment. The former indicates the present system reliability situation and the latter points the way to reliability improvement efforts. In this scheme, the problem is described and modeled by a dynamic directed network. Consider that the actual processing time of machines is to contribute to failure occurrence, it is used to calculate the failure times and failure losses. The obtained total failure times and failure losses of the system are applied to evaluate its reliability. Techniques to estimate two kinds of failure losses based CIMs are presented. They offer guidelines to realize system reliability growth cost-effectively. A case study of a real job shop is provided as an example to demonstrate the validity of the proposed methods. Comparison to other commonly used methods shows the efficiency of the proposed methods.

Suggested Citation

  • Ding Zhang & Yingjie Zhang & Mingrang Yu & Yun Chen, 2017. "Reliability evaluation and component importance measure for manufacturing systems based on failure losses," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1859-1869, December.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:8:d:10.1007_s10845-015-1073-1
    DOI: 10.1007/s10845-015-1073-1
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    References listed on IDEAS

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    1. Zio, Enrico & Piccinelli, Roberta, 2010. "Randomized flow model and centrality measure for electrical power transmission network analysis," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 379-385.
    2. Sun, Ji-wen & Xi, Li-feng & Du, Shi-chang & Ju, Bo, 2008. "Reliability modeling and analysis of serial-parallel hybrid multi-operational manufacturing system considering dimensional quality, tool degradation and system configuration," International Journal of Production Economics, Elsevier, vol. 114(1), pages 149-164, July.
    3. Sadjadi, Seyed Jafar & Soltani, R., 2009. "An efficient heuristic versus a robust hybrid meta-heuristic for general framework of serial–parallel redundancy problem," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1703-1710.
    4. Rocco S., Claudio M. & Ramirez-Marquez, Jose Emmanuel, 2012. "Innovative approaches for addressing old challenges in component importance measures," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 123-130.
    5. Barker, Kash & Ramirez-Marquez, Jose Emmanuel & Rocco, Claudio M., 2013. "Resilience-based network component importance measures," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 89-97.
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    Cited by:

    1. Li, Wanhong & Liu, Guangzhong, 2022. "Dynamic failure mode analysis approach based on an improved Taguchi process capability index," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).

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