A Novel Maximum-Entropy Bayesian Integration Approach for Reliability Analysis

Reliability analysis based on data from various source is common today. Bayes theory is proved effectively in integrating prior information and field information. However, the complicated calculation and limited applicability have a negative effect on solution. And the fusion is imbalanced in some case. This paper investigates a novel approach to integrate degradation data and lifetime data for reliability analysis. Firstly, inverse Gaussian process model is adopted to model the degradation and the crude estimation can be solved by degradation data. After that, a constrained maximum-entropy Bayesian integration model is proposed for exploring more information from reliability life test. For simplifying the calculation, a pivot variable, failure probability, is defined and updated in this model. This allows us to derive the model parameters by fitting the failure probability curve rather than the calculation on Bayes posterior distribution. Accordingly, the reliability assessment can be conducted based on the inverse Gaussian process model. A case study illustrates the validity and improvement of the proposed method.