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Integrated Wavelet Neural Networks in Fault Diagnosis Based on Information Fusion

LUO Yue-gang 1, CHEN Chang-zheng 2, ZENG Hai-quan 1, WEN Bang-chun 1 (1. School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China; 2. Shenyang University of Technology, Shenyang 110023, China.  
The tight wavelet neural network was constituted taking the nonlinear Morlet wavelet radices as the stimulant function. The idiographic algorithm was presented. It can combine the advantages of wavelet analysis and neural networks. The integrated-wavelet neural-network fault-diagnosis system was set up based on both information fusion technology and actual fault diagnosis. The system takes the sub-wavelet neural network as primary diagnosis from different sides, and then gains the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given. The system takes full advantage of diversified characteristic information, and improves diagnosis rate.
【Fund】: 国家自然科学基金重大资助项目 ( 199905 10 )
【CateGory Index】: TP183
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