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Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol

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

Abstract

Condition assessments and rating systems are frequently used by field engineers to assess inland navigation assets and components. The goal of these assessments is to initiate effective risk-informed budget plans for maintenance and repair/replace. Ideally, a degradation model of every component failure mode in the gate would facilitate maintenance decision-making. However, sometimes there is no clear physical understanding how a damage progresses in time; for example, it isn't clear how the bearing gaps change in time in the quoin blocks of a miter gate. Therefore, this is one motivation for the framework proposed in this paper, which integrates Structural Health Monitoring with a Markov transition matrix built from historical condition assessment. To show the applicability of this framework, two examples are presented of how to find the optimal time to plan for maintenance of components in miter gates i) static maintenance planning based on operational condition assessment (OCA) ratings only and ii) dynamic maintenance planning based on integration of damage diagnostics based on monitoring data and failure prognosis based on OCA ratings. In addition, this paper presents a new Bayesian approach to estimate the ratio of errors in the OCA ratings, which allows for improved accuracy in OCA rating-based prognosis.

Suggested Citation

  • Vega, Manuel A. & Hu, Zhen & Todd, Michael D., 2020. "Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306487
    DOI: 10.1016/j.ress.2020.107147
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Chadha, Mayank & Ramancha, Mukesh K. & Vega, Manuel A. & Conte, Joel P. & Todd, Michael D., 2023. "The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Vega, Manuel A. & Hu, Zhen & Fillmore, Travis B. & Smith, Matthew D. & Todd, Michael D., 2021. "A Novel Framework for Integration of Abstracted Inspection Data and Structural Health Monitoring for Damage Prognosis of Miter Gates," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    4. 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).

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