Quantifying the value of negative inspection outcomes in fatigue maintenance planning: Cost reduction, risk mitigation and reliability growth

• Published in: Reliability engineering & system safety Vol. 226; p. 108668
• Main Authors: Zou, Guang, Kolios, Athanasios
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.10.2022; Elsevier BV
• Subjects: Bayesian decision optimization; Computer applications; Condition monitoring; Decision analysis; Decision making; Information technology; Inspection; Life cycle costs; Maintenance costs; Maintenance modeling; Nondestructive testing; Reliability analysis; Reliability engineering; Risk; Risk analysis; Risk reduction; Structural reliability; Value of information; Structural reliability; Bayesian decision optimization; Value of information; Risk analysis; Maintenance modeling; Life cycle costs
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2022.108668

•A probabilistic approach to quantify the value of negative inspection outcomes.•Influences of maintenance models on the value of information (VoI) are presented.•Sources of the VoI in different maintenance decision-making contexts are identified.•The VoI can be attributed to reductions of both maintenance costs and failure risk.•The VoI is more evident in case of imperfect maintenance & high-quality inspections. With the development of sensing, non-destructive evaluation and information technology, benefits of informed structural maintenance decision-making based on systematic decision analysis have been increasingly appreciated and required. This tendency necessitates methods for quantifying the value of information (VoI) provided by condition inspections (or monitoring) and fully utilizing the VoI, i.e., transforming information into improved maintenance decisions that add value. While positive inspection outcomes are often considered as indicators for maintenance actions, implications of negative outcomes on reliability, risk and costs are seldom quantified. The objective of this study is to investigate potential benefits of negative outcomes to maintenance costs, failure risk and reliability from a maintenance planning perspective by developing a probabilistic VoI computational method. A structural maintenance decision-making framework and a VoI computational method are developed, considering uncertainties associated with structural deterioration forecast and inspection outcomes. For the first time, influences of maintenance effect modeling on VoI computation are investigated. The framework and method are illustrated on a marine structure. It is concluded that the VoI from negative outcomes can be attributed to reductions of both maintenance costs and failure risk, especially when high-quality inspection techniques are used and maintenance effects are imperfect.