FAULT DIAGNOSIS BASED ON INTEGRATION OF CLUSTER ANALYSIS, ROUGH SET METHOD AND FUZZY NEURAL NETWORK

被引：3
|
作者：
Feng ZhipengDepartment of Precision Instrumentsand Mechanology
机构：
来源：
Chinese Journal of Mechanical Engineering | 2004年 / 03期
关键词：
Fault diagnosis Self-erganizing map Rough sets Adaptive neuro-fuzzy inference system;
D O I：
暂无
中图分类号：
TP277 [监视、报警、故障诊断系统];
学科分类号：
0804 ; 080401 ; 080402 ;
摘要：
In order to increase the efficiency and decrease the cost of machinery diagnosis, a hybrid system of computational intelligence methods is presented. Firstly, the continuous attributes in diagnosis decision system are discretized with the self-organizing map (SOM) neural network. Then, dynamic reducts are computed based on rough set method, and the key conditions for diagnosis are found according to the maximum cluster ratio. Lastly, according to the optimal reduct, the adaptive neuro-fuzzy inference system (ANFIS) is designed for fault identification. The diagnosis of a diesel verifies the feasibility of engineering applications.
引用
收藏
页码：349 / 352
页数：4
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