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Reliability Analysis of Special Vehicle Critical System Using Discrete-Time Bayesian Network
Author(s) -
Zong-Yuan Li,
GuangJun Jiang,
Hongxia Chen,
Haibin Li,
Honghua Sun
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/5579218
Subject(s) - reliability (semiconductor) , fault tree analysis , reliability engineering , process (computing) , bayesian network , dynamic bayesian network , computer science , fault (geology) , bayesian probability , complex system , data mining , engineering , machine learning , artificial intelligence , power (physics) , physics , quantum mechanics , seismology , geology , operating system
The reliability assessment of special vehicles has become very important. However, due to the special structure of special vehicles, it is difficult to collect a large amount of experimental data. The use of traditional fault tree analysis cannot accurately assess product reliability. In this paper, dynamic fault trees are used to model the critical systems of special vehicles, and discrete Bayesian networks are used to evaluate the reliability of critical systems of special vehicles, which solved the problems of difficulty in accurately describing complex systems in the process of system reliability analysis and difficulty in obtaining accurate data in the process of analysis. Finally, through the combination of expert experience and the evaluation of the calculation results, the rationality of the method used in this paper in the reliability evaluation of special vehicles is verified.

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