An uncertain information fusion method for fault diagnosis of complex system

被引:2
作者
Wang, HW [1 ]
Zhou, JL [1 ]
He, ZY [1 ]
Sha, JC [1 ]
机构
[1] Natl Univ Def Technol, Sch Humanities & Management, Changsha 410073, Hunan, Peoples R China
来源
2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年
关键词
Bayesian networks (BN); complex system; fault diagnosis; noisy-OR model; uncertain information fusion;
D O I
10.1109/ICMLC.2003.1259733
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Fault diagnosis is becoming extremely important for safety and high reliability of complex system. But the fault diagnosis for complex system is the decision with uncertainty under small sample. The characteristics of complex system fault diagnosis require utilizing all kinds of information adequately. BN provides a flexible means of representing and reasoning with probabilistic information. Uncertainty and dependences are easily incorporated in the analysis. In the article, the application of Bayesian networks (BN) for monitoring and diagnosis of complex system is described. Furthermore, we propose leaky Noisy-OR model to reduce the data requirements in BN inference. The advantages of BN model for complex system fault diagnosis are demonstrated through example.
引用
收藏
页码:1505 / 1510
页数:6
相关论文
共 19 条