A Bayesian condition-based maintenance and monitoring policy with variable sampling intervals. (February 2022)

• Record Type: Journal Article
• Authors: Kampitsis, Dimitris; Panagiotidou, Sofia
• Is Part Of: Reliability engineering & system safety. Volume 218:Part A(2022)
• Publication Date: 2022-02
• Subjects: Condition-based maintenance -- Condition-based monitoring -- Bayesian control chart -- Variable inspection interval -- Double sampling; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.108159
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Double-sampling Bayesian scheme with state-dependent inspection intervals. Markovian deterioration with self-announced failures. Double samplings are allowed. Analytic derivation of cost function without the need for discretization. Improved economic outcome compared to previous Bayesian policies. Abstract: The aim of this study is to propose a Condition-Based Maintenance and Monitoring (CBM) policy which employs a Bayesian inspection scheme. A single unit system, which is subject to both operational deterioration and catastrophic failures, is considered. The equipment may operate in two different non-observable states (healthy and unhealthy). The unhealthy state is characterized by higher operational cost and higher proneness to failure. Failures are self-announced (directly observable) and thus, corrective maintenance is implemented immediately. A new double-sampling Bayesian control chart with state-dependent variable inspection frequency is proposed. The process operation is analytically modeled through a six-state Markov process, while, unlike all previous Bayesian models, there is no need for discretization of the unhealthy-state probabilities. At each inspection instance all available information regarding the equipment condition is utilized in order to schedule future inspections and preventive maintenance actions and detect possible operation in the unhealthy state. The critical parameters, namely the duration of the inspection intervals, the sample sizes and the preventive maintenance times, which minimize the expected total cost per time unit, are determined. Numerical comparisons with three other Bayesian CBM models are conducted to demonstrate the effectiveness of the proposed policy.