Application of Bayesian Networks in Reliability Evaluation
By Bao-ping Cai, Xiangdi Kong, Yonghong Liu, Jing Lin, Xiaobing Yuan, Hongqi Xu, Renjie Ji. 2019
236 citations recorded by the source corpus.
The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.
Subfields: Safety, Risk, Reliability and Quality, Software, Statistics and Probability
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Citations in the corpus
- Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “Nevertheless, other approaches (like probabilistic networks) have shown good results in other contexts where images are not the inputs of the problem [19], [69].”