Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1140653 | Mathematics and Computers in Simulation | 2010 | 11 Pages |
Abstract
Geometric process modeling is a useful tool to study repairable deteriorating systems in maintenance problems. This model has been used in a variety of situations such as the determination of the optimal replacement policy and the optimal inspection-repair-replacement policy for standby systems, and the analysis of data with trend. In this article, Bayesian inference for the geometric process with several popular life distributions, for instance, the exponential distribution and the lognormal distribution, are studied. The Gibbs sampler and the Metropolis algorithm are used to compute the Bayes estimators of the parameters in the geometric process. Simulation results are presented to illustrate the use of our procedures.
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Jianwei Chen, Kim-Hung Li, Yeh Lam,