Preventive maintenance (PM) is an effective maintenance policy which is widely applied in industry. Reviews the main approaches of modelling PM and discusses the characteristics of real life PM data which influence the methods for modelling PM. The most salient features of these data are the limited size and intensive censoring effect. Then introduces a parametric bootstrap method for fitting PM data to distributions. A simulation study to compare this method with the established Akaike and Schwarz criteria shows that while the bootstrap method is marginally better in identifying the true distribution, this is counterbalanced by the intensive computational effort needed.
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Kobbacy et al. (1997) studied this question.
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