Bayesian information fusion method for reliability analysis with failure-time data and degradation data

Degradation data and failure-time data are two types of data commonly used in reliability analysis. Both types of data are collected from different sources for reliability analysis of complex products. For highly reliable products, however, it is often difficult to collect sufficient useful data for reliability analysis with high accuracy, which poses the challenge for small sample size problems, that is, single-type data with few samples. In this paper, three novel Bayesian information fusion models are first proposed to characterize the inherent relationship between the failure-time data and the degradation data, and further to integrate the heterogeneous data to obtain accurate reliability analysis results under small sample size. Then, a model selection method is developed to choose appropriate model from the Wiener process, gamma process, and IG process models. Finally, the reliability analysis is completed based on the parameter estimation of the Bayesian information fusion model with the aid of the MCMC method. An industrial example is presented to demonstrate the effectiveness of the proposed Bayesian information fusion method.