The signal model: A model for competing risks of opportunistic maintenance

• Published in: European journal of operational research 2011-11, Vol.214 (3), p.665-673
• Main Authors: Bedford, Tim, Dewan, Isha, Meilijson, Isaac, Zitrou, Athena
• Format: Article
• Language: English
• Subjects: Applied sciences; Collection; Competing risks; Decision making models; Decision theory. Utility theory; Deterioration; Estimates; Exact sciences and technology; Maintenance; Mathematics; Maximum likelihood method; Operational research; Operational research and scientific management; Operational research. Management science; Optimization; Optimization techniques; Parametric inference; Preventive maintenance; Probability and statistics; Reliability; Reliability Maintenance Competing risks Statistical inference; Reliability theory. Replacement problems; Risk; Risk theory. Actuarial science; Sciences and techniques of general use; Statistical inference; Statistics; Studies
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2011.05.016

►We present a reliability model for a system that releases signals as it degrades. ►The released signals are used to inform opportunistic maintenance. ►Maximum likelihood estimators for the parameters are derived from censored data. ►The system lifetime is determined without having to assume a dependence structure. ►The model can be used to support decisions in optimising preventive maintenance. This paper presents a competing risks reliability model for a system that releases signals each time its condition deteriorates. The released signals are used to inform opportunistic maintenance. The model provides a framework for the determination of the underlying system lifetime from right-censored data, without requiring explicit assumptions about the type of censoring to be made. The parameters of the model are estimated from observational data by using maximum likelihood estimation. We illustrate the estimation process through a simulation study. The proposed signal model can be used to support decision-making in optimising preventive maintenance: at a component level, estimates of the underlying failure distribution can be used to identify the critical signal that would trigger maintenance of the individual component; at a multi-component system level, accurate estimates of the component underlying lifetimes are important when making general maintenance decisions. The benefit of good estimation from censored data, when adequate knowledge about the dependence structure is not available, may justify the additional data collection cost in cases where full signal data is not available.