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A resilience measure formulation that considers sensor faults

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  • Yoo, Minji
  • Kim, Taejin
  • Yoon, Joung Taek
  • Kim, Yunhan
  • Kim, Sooho
  • Youn, Byeng D.

Abstract

Resilience is the probability that the system will not fail through resistance and recovery efforts. Most resilience studies to date have been performed with an assumption of no false alarms. However, in real-world settings, there are many possible causes of false alarms; one major cause is sensor faults. Therefore, this study proposes a newly formulated engineering resilience measure that considers sensor faults. The proposed measure is formulated in a probabilistic manner, and includes accurate system health state estimation, system reliability, and sensor reliability. In this research, the effectiveness of the proposed resilience measure is demonstrated by implementing prognostics and health management (PHM) into an electro-hydrostatic actuator (EHA). In the system, the sensor states affect the resilience of the system by misjudging the estimation of the system health state. The study shows how the proposed idea correctly estimates the resilience of the system under sensor degradation and fault. Finally, the accuracy of the proposed measure is compared with the two prior resilience measures. It is determined that the results of the proposed measure are superior for systems with low sensor reliability.

Suggested Citation

  • Yoo, Minji & Kim, Taejin & Yoon, Joung Taek & Kim, Yunhan & Kim, Sooho & Youn, Byeng D., 2020. "A resilience measure formulation that considers sensor faults," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:reensy:v:199:y:2020:i:c:s0951832017311031
    DOI: 10.1016/j.ress.2019.02.025
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    References listed on IDEAS

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    1. Yoon, Joung Taek & Youn, Byeng D. & Yoo, Minji & Kim, Yunhan, 2017. "A newly formulated resilience measure that considers false alarms," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 417-427.
    2. Shan Li & Yong Chen, 2009. "Sensor fault detection for manufacturing quality control," IISE Transactions, Taylor & Francis Journals, vol. 41(7), pages 605-614.
    3. Jensen, H.A. & Muñoz, A. & Papadimitriou, C. & Millas, E., 2016. "Model-reduction techniques for reliability-based design problems of complex structural systems," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 204-217.
    4. Christian Habermann & Fabian Kindermann, 2007. "Multidimensional Spline Interpolation: Theory and Applications," Computational Economics, Springer;Society for Computational Economics, vol. 30(2), pages 153-169, September.
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    Cited by:

    1. Mukhopadhyay, Koushiki & Liu, Bin & Bedford, Tim & Finkelstein, Maxim, 2023. "Remaining lifetime of degrading systems continuously monitored by degrading sensors," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Wang, Ying & Zheng, Xueke & Wang, Le & Lu, Gavin & Jia, Yixing & Li, Kezhi & Li, Mian, 2023. "Sensor fault detection of vehicle suspension systems based on transmissibility operators and Neyman–Pearson test," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    4. Ahmadi, Somayeh & Saboohi, Yadollah & Vakili, Ali, 2021. "Frameworks, quantitative indicators, characters, and modeling approaches to analysis of energy system resilience: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    5. Saeed, Umer & Jan, Sana Ullah & Lee, Young-Doo & Koo, Insoo, 2021. "Fault diagnosis based on extremely randomized trees in wireless sensor networks," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    6. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Improving energy efficiency prediction under aberrant measurement using deep compensation networks: A case study of petrochemical process," Energy, Elsevier, vol. 263(PC).

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