Maintenance Decision-Making for Systems Operating Under Indirect Condition Monitoring: Value of Online Information and Impact of Measurement Uncertainty
This paper deals with maintenance decision-making for single-unit deteriorating systems operating under indirect condition monitoring. Based on a degradation and measurement model of crack growth propagation, two new maintenance policies using prognostic information are introduced. Their maintenance cost models are evaluated jointly by analytical and simulation approaches, and are compared with two more classical benchmark models. Such complete models integrating degradation phenomenon, monitoring characteristics, state estimation, prognostics, and maintenance assessment can give rise to fruitful numerical analyses and discussions. The main contributions of the paper are to i) analyze jointly the condition-based and dynamic structure of the considered maintenance policies; ii) propose some effective methods to reduce the effect of measurement uncertainty in condition-based maintenance decision-making; and iii) show the relevance of quantification methods when deciding to resort to prognostic approaches, and to invest in condition monitoring devices.
indirect condition monitoring measurement uncertainty degradation maintenance policy prognostic particle filter real-time estimation