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.
This paper deals with the maintenance optimization of a system subject to a stressful environment. The system deterioration behaviour can be modified by the environment; the degradation mode can change due to the random evolution of the stressful environment. Reciprocally, the environment conditions can be influenced by the system state and as a consequence, a change in the environment can be an indicator of the system state. This paper describes a condition-based maintenance decision framework to tackle the potential variations in the system deterioration, and especially in the deterioration rate, and the new information on the system state given by the evolution of the environmental variables.
The aim of this paper is to discuss the modelling and optimization of condition-based maintenance policies for systems is submitted to usage profile changes. The considered system undergoes a monotone deterioration (gamma process) and its is impacted by the usage conditions (covariates) via the proportional hazards model. Four different policies are proposed and the optimal maintenance parameters minimising the long run average maintenance cost are derived. The different maintenance policies are numerically compared by Monte Carlo simulations.
This paper deals with a gradually deteriorating system operating under an uncertain environment whose state is only known on a finite rolling horizon. As such, the system is subject to constraints. Maintenance actions can only be planned at imposed times called maintenance opportunities that are available on a limited visibility horizon. This system can, for example, be a commercial vehicle with a monitored critical component that can be maintained only in some specific workshops. Based on the considered system, we aim to use the monitoring data and the time-limited information for maintenance decision support in order to reduce its costs. We propose two predictive maintenance policies based, respectively, on cost and reliability criteria. Classical age-based and condition-based policies are considered as benchmarks. The performance assessment shows the value of the different types of information and the best way to use them in maintenance decision making.
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