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Model correction and updating of a stochastic degradation model for failure prognostics of miter gates

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  • Jiang, Chen
  • Vega, Manuel A.
  • Todd, Michael D.
  • Hu, Zhen

Abstract

Understanding the degradation of the quoin block is vital for failure prognostics in miter gates. Due to the complicated degradation mechanism, degradation models based on simplifications and assumptions cannot accurately describe the damage evolution. It is observed that small errors in a simplified degradation model can lead to a large discrepancy in the remaining useful life estimation attributed to error accumulation over time. Aiming to address this issue in failure prognostics, this paper presents a dynamic model correction framework for a simplified degradation model using strain measurements. In the proposed framework, a polynomial chaos expansion (PCE) model is employed to compensate the missing physics in a simplified stochastic degradation model. A maximum likelihood estimation method is developed to estimate the uncertain parameters of the simplified physics-based degradation model along with the unknown PCE model parameters using strain measurements as the observables. The updated damage degradation model is then applied to failure prognostics of a miter gate. Results of a case study show that the proposed approach can effectively improve the accuracy of failure prognostics in miter gates.

Suggested Citation

  • Jiang, Chen & Vega, Manuel A. & Todd, Michael D. & Hu, Zhen, 2022. "Model correction and updating of a stochastic degradation model for failure prognostics of miter gates," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    • Handle: RePEc:eee:reensy:v:218:y:2022:i:pa:s0951832021006839
    DOI: 10.1016/j.ress.2021.108203
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    References listed on IDEAS

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    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).
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    5. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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