System Reliability Evaluation In Information Fusion Method And Its Application

In this paper, when there has few samples, we discuss the estimation of the system reliability in multi-sources of prior information with Bayes, EB (Empirical Bayes) and HB (Hierarchical Bayes) theories. Then we study the robustness of prior distribution in Bayes statistical analysis. The results from simulation show that the method proposed in this paper is effective and reasonable.The main workis:Firstly, When prior information comes from different sources.we develop some methods to realize the fusion of the system information based on the correlation function, the credibility and the ideology of the maximum like hood. And confirm weights of every prior distribution logically when fusing.Secondly, methods for pooling failure rate data obtained from different sources is discussed, introduce how to use the EB and HB theories to realize the fusion information from multi-sources, and find the prior distribution and posterior distribution of the failure rate. Then the EB estimate of the failure rate is obtained using the squared error loss and the Linex loss functions.Thirdly, When prior information comes from different sources, the EB and HB fusion methods of prior information and their applications in reliability analysis of k/n (G) system are discussed, and then give the Bayes estimation.Finally, the optimal Bayes robust credible set and the optimal Bayes robust point estimator of the failure rate X are discussed using the r- posterior expected loss under the squared error loss as the criterion.