Data-Based Modeling of the Failure Rate of Repairable Equipment

A databaseof failures of many types of medical equipment was analysed,to study the dependence of failure rate on equipment age andon time since repair. The intention was to use this large datasetto assess the validity of some widely-used models of failurerate, such as the power-law and loglinear Poisson processes,and so to recommend simple and adequate models to those practitionershaving little data to discriminate between rival models. Theaim is also to illustrate a methodology for computing policycosts from failure databases. The power-law process model wasfound to fit slightly better overall than did the loglinear andlinear processes. Some related models were created to fit anobserved peaking of failure rate. The data showed a decreasinghazard of (first) failure after repair for some equipment types.This can be due to imperfect or hazardous repair, and also todiffering failure rates among a population of machines. Two simplemodels of imperfect repair were used to fit the data, and anEmpirical Bayes method was used to fit a model of variable failurerate between machines. Neglect of such variation can lead toan over-estimate of the hazardousness of repair.