Fault Diagnosis of Rotating Machinery Using Knowledge-Based Fuzzy Neural Network

LI Ru-qiang; CHEN Jin; WU Xing

Volume 27 Issue 1

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Article Navigation > Applied Mathematics and Mechanics > 2006 > 27(1): 89-97

LI Ru-qiang, CHEN Jin, WU Xing. Fault Diagnosis of Rotating Machinery Using Knowledge-Based Fuzzy Neural Network[J]. Applied Mathematics and Mechanics, 2006, 27(1): 89-97.

Citation:

LI Ru-qiang, CHEN Jin, WU Xing. Fault Diagnosis of Rotating Machinery Using Knowledge-Based Fuzzy Neural Network[J]. Applied Mathematics and Mechanics, 2006, 27(1): 89-97.

Citation:

LI Ru-qiang, CHEN Jin, WU Xing. Fault Diagnosis of Rotating Machinery Using Knowledge-Based Fuzzy Neural Network[J]. Applied Mathematics and Mechanics, 2006, 27(1): 89-97.

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Fault Diagnosis of Rotating Machinery Using Knowledge-Based Fuzzy Neural Network

The State Key Laboratory of Vibration, Shock & Noise, Shanghai Jiaotong University, Shanghai, 200030, P. R. China

Received Date: 2004-10-11
Rev Recd Date: 2005-08-23
Publish Date: 2006-01-15

Abstract

Abstract

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.
- rotating machinery,
- fault diagnosis,
- rough sets theory,
- fuzzy sets theory,
- generic algorithm,
- knowledge_based fuzzy neural network

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References(12)

References

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