| FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK |
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| 作者姓名： | 李如强 陈进 伍星 |
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| 作者单位： | The State Key Laboratory of Vibration，The State Key Laboratory of Vibration，The State Key Laboratory of Vibration Shock and Noise，Shanghai Jiaotong University，Shanghai 200030，P.R.China，Shock and Noise，Shanghai Jiaotong University，Shanghai 200030，P.R.China，Shock and Noise，Shanghai Jiaotong University，Shanghai 200030，P.R.China |
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| 基金项目： | Project supported by the National Major Science and Technology Foundation of China during the 10th Five-Year Plan Period （No.2001BA204B05-KHK Z0009） |
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| 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.
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| 关 键 词： | 故障诊断 转动式机械 模糊神经系统网络 模糊理论 |
| 收稿时间： | 2003-10-11 |
| 修稿时间： | 2005-08-23 |
Fault diagnosis of rotating machinery using knowledge-based fuzzy neural network |
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| Ru-qiang Li,Jin Chen,Xing Wu.FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK[J].Applied Mathematics and Mechanics(English Edition),2006,27(1):99-108. |
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| Authors: | Ru-qiang Li Jin Chen Xing Wu |
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| Institution: | The State Key Laboratory of Vibration, Shock and Noise, Shanghai Jiaotong University,Shanghai 200030, P. R. China |
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| 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. |
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| Keywords: | rotating machinery fault diagnosis rough sets theory fuzzy sets theory generic algorithm knowledge-based fuzzy neural network |
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