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Risk assessment of sensor failures in a condition monitoring process; degradation-based failure probability determination

Author

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  • Farzin Salehpour-Oskouei

    (Sahand University of Technology)

  • Mohamad Pourgol-Mohammad

    (Sahand University of Technology)

Abstract

Condition monitoring of a system is in need of an efficient sensor network for detecting system faults in early stages. Sensor is a silent-failure component as it is not monitored in the main system misleading in estimation of the system health state. Then, failure-causes of the system are not detected due to missed detection. A risk-based measure is proposed in this research to take into account the consequence of sensor missed-detection. All possible combinations are determined for failure-causes as components state vectors. The probability of missed-detection is estimated by applying occurrence probability of failure causes and degradation-based failure probability of sensors. First, a sensor characteristic is selected through which the degradation process is affected. Then a degradation model is developed to calculate the sensor time to failure. The consequences of missed-detection are also corresponding quantifiable potential losses through both failure costs and maintenance expenditure. Finally, all feasible sensors placement scenarios are compared due to proposed risk measure. As a case study, sensor network of steam turbine condition monitoring is selected. Various sensor placement scenarios of steam turbine are prioritized based on the risk index and results are discussed.

Suggested Citation

  • Farzin Salehpour-Oskouei & Mohamad Pourgol-Mohammad, 2017. "Risk assessment of sensor failures in a condition monitoring process; degradation-based failure probability determination," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(3), pages 584-593, September.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:3:d:10.1007_s13198-017-0573-0
    DOI: 10.1007/s13198-017-0573-0
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    Citations

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    Cited by:

    1. Bahareh Tajiani & Jørn Vatn, 2023. "Adaptive remaining useful life prediction framework with stochastic failure threshold for experimental bearings with different lifetimes under contaminated condition," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1756-1777, October.
    2. Dinh, Duc-Hanh & Do, Phuc & Hoang, Van-Thanh & Vo, Nhu-Thanh & Bang, Tao Quang, 2024. "A predictive maintenance policy for manufacturing systems considering degradation of health monitoring device," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    3. 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.
    4. Mohammad Pourgol-Mohammad & Farzin Salehpour-Oskouei, 2023. "Multi-criteria sensor placement determination in prognostics and health management using combined fault diagnosis, fault detection, and risk indexes," Journal of Risk and Reliability, , vol. 237(6), pages 1234-1247, December.

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