Continuous-Time Predictive Maintenance Modeling with Dynamic Decision Framework

Abstract

Digital technologies improve the information collected on systems and allow the development of condition-based maintenance policies and models using the remaining useful life. Accordingly, maintenance policies have evolved from a simple time-based to a more complex and competitive predictive approach. However, considering a dynamic maintenance decision framework with a self-adaptive decision rule has not been thoroughly addressed. This chapter deals with continuously deteriorating systems and focuses on dynamic maintenance policies, i.e., policies using real-time information to update the decision rule and handle the model’s uncertainty. The first part presents popular stochastic processes for degradation modeling and condition-based maintenance decision rule. Then, dynamic maintenance policies are described in two different contexts: for groupings of maintenance actions and for reducing uncertainty in modeling. Finally, a particular case of dynamic preventive maintenance model is described in detail for a system with continuous degradation and unknown degradation parameters. It is based on the inverse Gaussian process with a nonperiodic inspection policy and includes parameters update.