6533b85cfe1ef96bd12bc9c6

RESEARCH PRODUCT

A predictive maintenance policy with imperfect monitoring

subject

description

For many systems,failure is a very dangerous or costly event. To reduce the occurrence of this event,it is necessary to implement a preventive maintenance policy to replace the critical elements before failure.Since elements do not often exhibit incipient faults, they are replaced before a complete exploiting of their useful life.To conjugate the objective of exploiting elements for almost all their useful life with the objective to avoid failure,condition based and,more recently,predictive maintenance policies have been proposed.This paper deals with this topic and proposes a procedure for the computation of the maintenance time that minimizes the global maintenance cost.By adopting a stochastic model for the degradation process and by hypothesizing the use of an imperfect monitoring system, the procedure updates by a Bayesian approach, thea-priori information, using the data coming from the monitoring system.The convenience in adopting the proposed policy,with respect to the classical preventive one,is explored by simulation,showing how it depends on some parameters characterizing the problem. For many systems, failure is a very dangerous or costly event. To reduce the occurrence of this event, it is necessary to implement a preventive maintenance policy to replace the critical elements before failure. Since elements do not often exhibit incipient faults, they are replaced before a complete exploiting of their useful life. To conjugate the objective of exploiting elements for almost all their useful life with the objective to avoid failure, condition based and, more recently, predictive maintenance policies have been proposed. This paper deals with this topic and proposes a procedure for the computation of the maintenance time that minimizes the global maintenance cost. By adopting a stochastic model for the degradation process and by hypothesizing the use of an imperfect monitoring system, the procedure updates by a Bayesian approach, the a-priori information, using the data coming from the monitoring system. The convenience in adopting the proposed policy, with respect to the classical preventive one, is explored by simulation, showing how it depends on some parameters characterizing the problem.