Fuzzy Bayesian network inference fault diagnosis of complex equipment based on fault tree

被引：6
|
作者：
Chen H. [1 ]
Zhao A. [1 ]
Li T. [1 ]
Cai C. [1 ]
Cheng S. [1 ]
Xu C. [1 ,2 ]
机构：
[1] College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] Nanjing CenLaser Laser Technology Company Limited, Nanjing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 05期
关键词：
Complex equipment; Fault diagnose; Fault tree; Fuzzy Bayesian network;
D O I：
10.12305/j.issn.1001-506X.2021.05.12
中图分类号：
学科分类号：
摘要：
The small-lot and customized attributes of complex equipment determines that there are relatively many uncertainties in the process of its life cycle, so potential fault hazard of complex equipment is inevitable, and the fault diagnosis of complex equipment is particularly important. Therefore, a fuzzy Bayesian network inference model for complex equipment fault diagnosis based on fault tree is proposed. First, the fault tree model for complex equipment is established by analyzing its structural composition. Secondly, the fault tree transformation method is used to construct the Bayesian network topology structure based on fault tree. Then, in view of lacking of structural data of complex equipment and the uncertainty of expert scoring, the fuzzy set theory is used to determine the parameters such as conditional probability. Finally, a case study is carried out, and the fault nodes (potential faults) of the cases are diagnosed by using causal inference and diagnostic inference in fuzzy Bayesian network inference, which is proved effective. The research results not only solves the problem that it is not practical to construct the optimal network by using search function in Bayesian network, but also solves the problem of data deficiencies of complex equipment and uncertainty of expert scoring by using fuzzy set theory. The proposed model is suitable not only for determining fault in process diagnosis, but also recognizing potential risk in advance diagnosis, and also play a role in the detection and evaluation of the improvement effect of faults (or potential faults) nodes. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：1248 / 1261
页数：13
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