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Bayesian inference with overlapping data for systems with continuous life metrics

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  • Jackson, Chris
  • Mosleh, Ali

Abstract

A Bayesian approach for generating inference from multiple overlapping higher level system data sets on component reliability parameters within systems with continuous life metrics (as distinct from on-demand systems) is presented in this paper. Overlapping data sets are those that are drawn simultaneously from the same process or system. The methodology proposed in this paper is exclusively based on time, but is easily transferrable to any other variable (such as distance, flow etc.). The approach is able to incorporate overlapping evidence from systems with continuous life metrics using a detailed understanding of the system logic represented using fault-trees, reliability block diagrams or equivalent representation. The reliability parameters of each component define the continuous reliability function associated with each sensor location. This paper offers a fully Bayesian method of analyzing multiple overlapping higher level data sets for complex systems with multiple instances of identical components. The scope of the paper is limited to binary-state systems and components that exist in either ‘failed’ or ‘successful’ states.

Suggested Citation

  • Jackson, Chris & Mosleh, Ali, 2012. "Bayesian inference with overlapping data for systems with continuous life metrics," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 217-231.
    • Handle: RePEc:eee:reensy:v:106:y:2012:i:c:p:217-231
    DOI: 10.1016/j.ress.2012.04.006
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    References listed on IDEAS

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    1. Graves, T.L. & Hamada, M.S. & Klamann, R. & Koehler, A. & Martz, H.F., 2007. "A fully Bayesian approach for combining multi-level information in multi-state fault tree quantification," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1476-1483.
    2. Graves, T.L. & Hamada, M.S. & Klamann, R.M. & Koehler, A.C. & Martz, H.F., 2008. "Using simultaneous higher-level and partial lower-level data in reliability assessments," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1273-1279.
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    Cited by:

    1. Jiang, Tao & Liu, Yu, 2017. "Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 3-15.
    2. Farzin Salehpour-Oskouei & Mohammad Pourgol-Mohammad, 2018. "Sensor placement determination in system health monitoring process based on dual information risk and uncertainty criteria," Journal of Risk and Reliability, , vol. 232(1), pages 65-81, February.
    3. Jackson, Chris & Mosleh, Ali, 2016. "Bayesian inference with overlapping data: Reliability estimation of multi-state on-demand continuous life metric systems with uncertain evidence," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 124-135.
    4. Yang, Lechang & Wang, Pidong & Wang, Qiang & Bi, Sifeng & Peng, Rui & Behrensdorf, Jasper & Beer, Michael, 2021. "Reliability analysis of a complex system with hybrid structures and multi-level dependent life metrics," Reliability Engineering and System Safety, Elsevier, vol. 209(C).

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