Bayes Inference for General Repairable Systems

Abstract

Models for repairable systems are often characterized by the assumed effect of a failure and the subsequent repair. As-bad-as-old models lead to the nonhomogeneous Poisson process and as-good-as-new models lead to the renewal process. We study Bayesian methods for some models that are a compromise between the bad-as-old and the good-as-new models. For the case of multiple systems, we consider a hierarchical Bayes model. We use Markov chain Monto Carlo methods to approximate properties of the posterior distributions.

• Hierarchical Bayes Model
• Imperfect Repair
• Markov Chain Monte Carlo
• Power Law Process

Additional information

Notes on contributors

Rong Pan

Dr. Pan is Assistant Professor in the Department of Industrial Engineering and is a Senior Member of ASQ. His email address is [email protected].

Steven E. Rigdon

Dr. Rigdon is Professor in the Department of Mathematics and Statistics and is a Senior Member of ASQ. His email address is [email protected].