Abstract
Bayesian Hierarchical Model-Based Information Fusion for Degradation Analysis Considering Non-Competing Relationship
Highlights
Nowadays, the complex systems such as industrial, transportation and military are generally required long-life and high-reliability [1]–[4]
Due to the advantages in the reliability assessment of long-life, high-reliability products, degradation analysis methods have been widely used in academic research and industrial application [8]
The Markov chain Monte Carlo (MCMC) method is employed for simulating posterior samples of parameters, The OpengBUGS is adopted to facilitate the implementation of MCMC for the Bayesian hierarchical model-based information fusion degradation analysis of the wear degradation data
Summary
The complex systems such as industrial, transportation and military are generally required long-life and high-reliability [1]–[4] Methods such as degradation analysis have been developed to enhance the reliability analysis of these systems [5]–[7]. Due to the advantages in the reliability assessment of long-life, high-reliability products, degradation analysis methods have been widely used in academic research and industrial application [8]. The complex products with long life and high reliability may have multiple performance degradation processes. The competing relationship multiple performance characteristics, and Bayesian method is used for parameters estimation. A hierarchical Bayesian method of gamma process model is introduced into this paper for modeling and analysis degradation process. The CDF and PDF of B-S distribution can be given as
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