Predictive Maintenance using Dynamic Probabilistic Networks

We study dynamic reliability of systems where system components age with a constant failure rate and there is a budget constraint. We develop a methodology to effectively prepare a predictive maintenance plan of such a system using dynamic Bayesian networks (DBNs). DBN representation allows monitoring the system reliability in a given planning horizon and predicting the system state under different replacement plans. When the system reliability falls below a predetermined threshold value, component replacements are planned such that maintenance budget is not exceeded. The decision of which component(s) to replace is an important issue since it affects future system reliability and consequently the next time to do replacement. Component marginal probabilities given the system state are used to determine which component(s) to replace. Two approaches are proposed in calculating the marginal probabilities of components. The first is a myopic approach where the only evidence belongs to the current planning time. The second is a look-ahead approach where all the subsequent time intervals are included as evidence.