Optimal Corrective Maintenance Policy Encountering Competing Risks Using Machine Learning Algorithms
Résumé
In this paper, a series system is considered which is periodically inspected. At inspection times, the failed components
are replaced by a new one. Therefore, from the component point of view, maintenance is perfect and from the
system point of view, maintenance is imperfect. Three different scenarios related to the components lifetime
distribution are considered. Firstly, the lifetime distribution of the components and their parameters are assumed
to be known, secondly, the lifetime distributions are assumed to be known with unknown parameters and, thirdly
lifetime distributions are considered to be unknown. A cost-based maintenance optimization is carried out. The
inspection interval is the decision parameter in all scenarios. The first scenario is considered as a benchmark to
other scenarios. For the second and third scenarios, machine learning algorithms are used to estimate the decision
parameter and to derive the long-run average total maintenance cost. Different cases are compared through numerical
implementations.