A system subject to several deteriorationprocesses is studiem. These processes arrive to the system following a Cox process and they grow according to a homogeneous gamma process. The system is failed when a degradation process exceeds a certain failure threshold. The maintenance strategy implemented on the system is condition-based maintenance, the deterioration state of the system is checked, and replacements are performer if necessary. A random effects model is considered to dealwith the heterogeneities between processes, in particular, a uniform distribution is used to model the inverse of the scale parameter of the gamma process. Finally, the analytic cost model is obtained and analysed through some numerical examples.
Many systems are subject to multiple degradation processes, which reduce their capability for fulfilling their functions. Pitting corrosion, which consists of the appearance of different that evolve simultaneously in the system, is a classic example of this multiple degradation. It is assumed that the system fails when the pits are large enough that do not allow the system to perform its function, or, in other words, when the degradation level has exceeded a certain fixed threshold, which indicates whether the system is in a good condition. For a system to work properly, a maintenance is performed on it: periodic inspections, repairs, and replacements of the components. The search of the optimal maintenance strategy is a key challenge since we must bear in mind the different costs associated to each maintenance task and the different stochastic processes influencing the system condition: the arrival processes and the growth processes. In this work, we study a system subject to different degradation processes, in which the arrival of those processes is modelled using Cox processes, which are generalizations of the non-homogeneous Poisson process. Using their properties, the survival function, the expected number of arrivals and the expected intensity are obtained.
A complex system consisting of monitored and non-monitored components is analyzed. Monitored components are subject to a degradation gamma process. Non-monitored components are subject to external failures. A Condition-Based Maintenance and an inspection policy are applied to reduce the impact of the failures in the monitored components. When a failure occurs, maintenance team performs a corrective replacement after a certain delay time. An opportunistic maintenance strategy is also implemented, meaning that a maintenance intervention can be used as an opportunity for preventive maintenance of monitored components. Each maintenance task implies a certain cost and each monitored component is assumed to provide a reward. The expected cost of the whole system is minimized through the optimization of the preventive thresholds and the time between inspections. Numerical examples are obtained from applying a blend of Genetic Algorithm and Monte-Carlo simulation.
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