Fault diagnosis of rotating machinery using knowledge-based fuzzy neural network

被引：19
|
作者：
Li, RQ [1 ]
Chen, J [1 ]
Wu, X [1 ]
机构：
[1] Shanghai Jiao Tong Univ, State Key Lab Vibrat Shock & Noise, Shanghai 200030, Peoples R China
来源：
APPLIED MATHEMATICS AND MECHANICS-ENGLISH EDITION | 2006年 / 27卷 / 01期
关键词：
rotating machinery; fault diagnosis; rough sets theory; fuzzy sets theory; generic algorithm; knowledge-based fuzzy neural network;
D O I：
10.1007/s10483-006-0113-1
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A novel knowledge-based fuzzy neural network (KBFNN) for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.
引用
收藏
页码：99 / 108
页数：10
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