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SUMMARYThis paper studies a condition-based maintenance policy for a repairable system subject to a continuousstate gradual deterioration monitored by sequential non-periodic inspections. The system can be maintained using different maintenance operations (partial repair, as good as new replacement) with different effects (on the system state), costs and durations. A parametric decision framework (multithreshold policy) is proposed to choose sequentially the best maintenance actions and to schedule the future inspections, using the on-line monitoring information on the system deterioration level gained from the current inspection. Taking advantage of the semi-regenerative (or Markov renewal) properties of the maintained system state, we construct a stochastic model of the time behaviour of the maintained system at steady state. This stochastic model allows to evaluate several performance criteria for the maintenance policy such as the long-run system availability and the long-run expected maintenance cost. Numerical experiments illustrate the behaviour of the proposed condition-based maintenance policy.

This paper deals with a predictive maintenance policy for a continuously deteriorating system subject to stress. We consider a system with two failure mechanisms which are respectively due to an excessive deterioration level and a shock. To optimize the maintenance policy of the system, an approach combining Statistical Process Control (SPC) and Condition-Based Maintenance (CBM) is proposed. CBM policy is used to inspect and replace the system according to the observed deterioration level. SPC is used to monitor the stress covariate. In order to assess the performance of the proposed maintenance policy and to minimize the long-run expected maintenance cost per unit of time, a mathematical model for the maintained system cost is derived. Analysis based on numerical results are conducted to highlight the properties of the proposed maintenance policy in respect to the different maintenance parameters.