A Bayesian condition-based maintenance and monitoring policy with variable sampling intervals

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.

Automated summary

This study introduces a Condition-Based Maintenance and Monitoring (CBM) policy using a Bayesian inspection scheme for a single unit system. It proposes a new double-sampling Bayesian control chart and analyzes the operation process through a six-state Markov process without the need for discretization of unhealthy-state probabilities. Critical parameters to minimize total cost per time unit are determined, and numerical comparisons are made to demonstrate effectiveness compared to other CBM models.