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A Bayesian approach to sensor placement optimization and system reliability monitoring

Author

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  • Masoud Pourali
  • Ali Mosleh

Abstract

Sensors are being increasingly used to monitor the functional state of complex systems. Sensors are used to make observation of physical quantities. The measured quantities are expected to provide information about the state of the system, its subsystems, components, and internal and external physical parameters. A complex system normally requires many sensors to extract required information from the sensed environment. In most cases, the problem of optimal sensor placement is difficult, because it requires optimization under uncertainty. This research developed new algorithms for sensor placement optimization under uncertainty and utilized them in a new system reliability monitoring approach. The overall methodology is designed to answer important questions such as how to infer the reliability of a system based on a limited number of sensor information points at certain subsystems (upward propagation); how to infer the reliability of a subsystem based on knowledge of the reliability of a main system (downward propagation); how to infer the reliability of a subsystem based on knowledge of the reliability of other subsystems (distributed propagation); and what are the optimum locations of sensors to provide the best estimate of system reliability.

Suggested Citation

  • Masoud Pourali & Ali Mosleh, 2013. "A Bayesian approach to sensor placement optimization and system reliability monitoring," Journal of Risk and Reliability, , vol. 227(3), pages 327-347, June.
    • Handle: RePEc:sae:risrel:v:227:y:2013:i:3:p:327-347
    DOI: 10.1177/1748006X13485663
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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. Iamsumang, Chonlagarn & Mosleh, Ali & Modarres, Mohammad, 2018. "Monitoring and learning algorithms for dynamic hybrid Bayesian network in on-line system health management applications," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 118-129.

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