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Optimal Testing Resources Allocation for Improving Reliability Assessment of Non-repairable Multi-state Systems

In: Recent Advances in Multi-state Systems Reliability

Author

Listed:
  • Yu Liu

    (University of Electronic Science and Technology of China)

  • Tao Jiang

    (University of Electronic Science and Technology of China)

  • Peng Lin

    (The University of Hong Kong)

Abstract

Due to limited reliability testing resources (e.g., budget, time, and manpower etc.), the reliability of a sophisticated system may not be able to accurately estimated by insufficient reliability testing data. The book chapter explores the reliability testing resources allocation problem for multi-state systems, so as to improve the accuracy of reliability estimation of an entire system. The Bayesian reliability assessment method is used to infer the unknown parameters of multi-state components by merging subjective information and continuous/discontinuous inspection data. The performance of each candidate testing resources allocation scheme is evaluated by the proposed uncertainty quantification metrics. By introducing the surrogate model, i.e., kriging model, the computational burden in seeking the optimal testing resources allocation scheme can be greatly reduced. The effectiveness and efficiency of the proposed method are exemplified via two illustrative case.

Suggested Citation

  • Yu Liu & Tao Jiang & Peng Lin, 2018. "Optimal Testing Resources Allocation for Improving Reliability Assessment of Non-repairable Multi-state Systems," Springer Series in Reliability Engineering, in: Anatoly Lisnianski & Ilia Frenkel & Alex Karagrigoriou (ed.), Recent Advances in Multi-state Systems Reliability, pages 241-264, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-319-63423-4_13
    DOI: 10.1007/978-3-319-63423-4_13
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