Joint assessment of ageing and efficiency of corrective and planned preventive maintenance

International audienceStatistical modelling and analysis are essential decision making tools for engineers concerned with the reliability and maintenance of complex systems. The dependability of repairable systems depends strongly on the efficiency of preventive and corrective maintenance actions. Corrective maintenance (CM), also called repair, is carried out after a failure and intends to put the system into a state in which it can perform its function again. Preventive maintenance (PM) is carried out when the system is operating and intends to slow down the wear process and reduce the frequency of occurrence of system failures. PM can be planned or condition-based. The basic assumptions on maintenance efficiency are known as minimal repair or As Bad As Old (ABAO) and perfect repair or As Good As New (AGAN). In the ABAO case, each maintenance leaves the system in the state it was before maintenance. In the AGAN case, each maintenance is perfect and leaves the system as if it were new. Obviously, reality is between these two extreme cases: standard maintenance reduces failure intensity but does not leave the system as good as new. This is known as imperfect maintenance. Many imperfect maintenance models have been proposed. Most of them, such as Kijimas's virtual age models, consider only one kind of maintenance. Rather few paper deal with the joint modelling of PM and CM. In a previous paper, we have proposed a general framework for the simultaneous modelling and assessment of the efficiency of corrective maintenance actions and condition-based preventive maintenance actions. The present paper proposes the same kind of results in the case of planned PM. First, the paper presents the properties of the maintenance process. Then, stochastic models for the efficiency of imperfect maintenance actions are developed based on a virtual age idea. The usual PM policies are replaced in the general framework. Statistical analysis of these models provides an assessment of maintenance efficiency and reliability indicators, from which engineering decisions can be made regarding the studied systems. Finally, we present an application to a real data set issued from electricity production systems, which uses a software tool that we have developed, MARS (Maintenance Assessment of Repairable Systems)