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

• Published in: Reliability engineering & system safety Vol. 204; p. 107147
• Main Authors: Vega, Manuel A., Hu, Zhen, Todd, Michael D.
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.12.2020; Elsevier BV
• Subjects: Assessments; Bayesian; Bayesian analysis; Damage; Damage estimation; Decision making; Failure modes; Maintenance; Markov process; Miter gates; Mitre gates; Motivation; Prognosis; Quoins; Ratings; Ratings & rankings; Reliability engineering; Remaining useful life; Repair & maintenance; Structural health monitoring; Surrogate model; Uncertainty quantification; Weibull analysis; Markov process; Miter gates; Surrogate model; Damage estimation; Weibull analysis; Bayesian; Uncertainty quantification; Remaining useful life
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2020.107147

•Condition-based maintenance strategies have become increasingly implemented.•Such strategies depend upon a combination of inspection and monitoring.•Human inspection data are often biased or missing.•Integrating these sources consistently leads to optimal maintenance strategies.•Human inspection biased or missing data-induced errors can be estimated. 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.