Adaptive condition-based maintenance models for deteriorating systems operating under variables environment and indirect condition monitoring
Abstract
The present paper deals with the efficient use of different types of covariate information in optimising condition-based maintenance decision-making for a deteriorating system operating under variable environment. The degradation phenomenon of system is the fatigue crack growth which is modelled by a physics-based stochastic process. The environment process is assumed to be a time-homogenous Markov chain with finite state space. We suppose that the environment state is observed perfectly, while the degradation state can be accessed only through an indirect monitoring technique. As such, two kinds of information are available at each inspection time: environmental covariate (external covariate) and diagnostic covariate (internal covariate). Based on this set of information, two condition-based maintenance policies adaptive to environment state are developed. In the first one, the adaptation approach is time-based, while in the second, it is condition-based. These maintenance strategies are compared with each other and with a classical non-adaptive one to point out the performances of each adaptation approach and hence the appreciation of using different information sources in maintenance decision-making.