Modeling and Sequential Repairs of Systems Considering Aging and Repair Effects

The reliability of a repairable system depends on the system age and the number of repairs it experienced. When these effects are considered, predicting the system reliability metrics, such as the cumulative number of failures and failure intensity in the future, becomes a challenging problem. Many existing models utilize Monte Carlo simulations to do prediction but this entails significant computational efforts. This chapter presents a modified Proportional Failure Intensity model to analyze repairable systems. By further modification (approximation) to the model, the system reliability metrics and the associated confidence bounds can be effectively predicted without conducting time-consuming simulations. Moreover, to make repair/replacement decisions, most research assumes the repair model of the system is available beforehand. In practice, however, the model needs to be estimated based upon failures and sequential repair/replacement decisions must be made based on the predicted system reliability metrics. The proposed model is utilized in this decision-making paradigm considering a short-run cost rate criterion. Unlike the widely used long-run cost rate, this criterion emphasizes the economic impact of a repair/replacement decision on the next fail-and-fix cycle of the system. Two benchmark data sets are analyzed to demonstrate the model in practical use.