Bayesian Fusion of Degradation and Failure Time Data for Reliability Assessment of Industrial Equipment Considering Individual Differences

Author:

Fu Guo-Zhong1ORCID,Zhang Xian12,Li Wei1,Guo Junyu3ORCID

Affiliation:

1. Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, China

2. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

3. School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China

Abstract

In the field of industrial equipment reliability assessment, dependency on either degradation or failure time data is common. However, practical applications often reveal that single-type reliability data for certain industrial equipment are insufficient for a comprehensive assessment. This paper introduces a Bayesian-fusion-based methodology to enhance the reliability assessment of industrial equipment. Operating within the hierarchical Bayesian framework, the method innovatively combines the Wiener process with available degradation and failure time data. It further integrates a random effects model to capture individual differences among equipment units. The robustness and applicability of this proposed method are substantiated through an in-depth case study analysis.

Funder

Natural Science Foundation of Sichuan, China

National Defense Science and Technology Key Laboratory Stabilization Support

Publisher

MDPI AG

Subject

Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering

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