In this study, an adaptive Bayesian decision model is developed to determine the optimal replacement age for the systems maintained according to a general age-replacement policy. It is assumed that when a failure occurs, it is either critical with probability p or noncritical with probability 1 − p, independently. A maintenance policy is considered where the noncritical failures are corrected with minimal repair and the system is replaced either at the first critical failure or at age τ, whichever occurs first. The aim is to find the optimal value of τ that minimizes the expected cost per unit time. Two adaptive Bayesian procedures that utilize different levels of information are proposed for sequentially updating the optimal replacement times. Posterior density/mass functions of the related variables are derived when the time to failure for the system can be expressed as a Weibull random variable. Some simulation results are also presented for illustration purposes.
