An Adaptive Bayesian Replacement Policy with Minimal Repair

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 non-critical with probability 1 - p, independently. A maintenance policy is considered where the non-critical failures are corrected with minimal repair and the system is replaced either at the first critical failure or at age whichever occurs first. The aim is to find the optimal value of which minimizes the expected cost per unit time. Two adaptive Bayesian procedures which 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.

• Type: Journal
• Year: 2002
• Where: IOR
• Authors: Savas Dayanik, Ülkü Gürler
• Keywords: Adaptive Bayesian Decision, IOR 2002, Optimal Replacement, Optimal Replacement Age