Optimal condition-based maintenance decisions for systems with dependent stochastic degradation of components

Components in engineered systems are subjected to stochastic deterioration due to uncertainties in the operating environmental conditions and material properties. The component could be, for example, moving parts such as gear boxes and pumps in a chemical plant or equipment production plant, the pipe segments in oil and gas pipeline system, and the heat transport pipe segment in nuclear power plants. The components need to be inspected and possibly replaced based on predetermined preventive or failure replacement criteria to achieve safe operation of the system. To this end, the stochastic degradation model of the component as well as the dependency of the degradation associated with different components has a potential impact on the decision-making on the optimal maintenance. In this study, the homogeneous gamma process is employed to model the stochastic degradation of each component involved in the system; the Gaussian copula function is used to characterize the dependency of the degradation of different components. The Monte Carlo simulation technique is used to evaluate the expected maintenance cost of the system considering the stochastic degradation model and dependency of the degradation based on a given maintenance policy. The minimum expected cost rule is adopted to select the optimal maintenance strategies, e.g. the optimal inspection interval. A numerical example including a two-component series system and a two-component parallel system is used to illustrate the developed probabilistic analysis approach and the impact of the dependency of the stochastic degradation on the preferred decisions.