A Cure Rate Model in Reliability for Complex System

This paper presents a new approach to do reliability analysis for complex system, where a certain fraction of the subsystems is defined as a "cure fraction" under the consideration that such subsystems are "longevous" compared with the entire system. Including introducing environment covariates and the joint power prior, the proposed model is developed with the Bayesian survival analysis method, and thus the problems for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo method based on Gibbs sampling is used to dynamically simulate the Markov chain of the parameters' posterior distribution. Finally, a numeric example is discussed to demonstrate the proposed model.