PARAMETRIC-ESTIMATION OF TIME-DEPENDENT FAILURE RATES FOR PROBABILISTIC RISK ASSESSMENT

This article describes a method for modeling a time-dependent failure rate, based on field data from similar components that are repeatedly restored to service after they fail. The method assumes that the failures follow a Poisson process, but allows many parametric models for the time-dependent failure rate-lambda(t). Ways to check many of the assumptions form an important part of the method. When the assumptions are satisfied, the maximum likelihood estimate of lambda(t) is approximately lognormal, and this lognormal distribution is appropriate to use as a Bayesian distribution for lambda(t) in a probabilistic risk assessment.