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《Control Theory & Applications》 2007-06
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Turbine rotor vibration faults diagnosis based on wavelet packet analysis and neural network

LIANG Ping, BAI Lei, LONG Xin-feng, FAN Li-li ( College of Electricity Power, South China University of Technology, Guangzhou Guangdong 510640, China; College of Chemical Engineering and Energy, South China University of Technology, Guangzhou Guangdong 510640, China; Electric Power Research Institute of Guangdong Power Grid, Guangzhou Guangdong 510600, China)  
According to the four typical fault signals of turbine vibration including rubbing, loosing, misalignment and mass unbalance collected from the Bently experiment table, energy analysis and symptom extraction are carried out by wavelet packet analysis. The results of analysis indicate that symptom extraction by wavelet packet analysis and energy decomposition can obtain the faults state of turbine rotor vibration, possess better differentiation capability of fault types. In addition, the fault symptom parameters extracted by wavelet packet decomposition and reconstruction condense the whole information of turbine rotor vibration faults, and neural network possesses good non-linear mapping capability. For these symptom parameters, applying BP neural network mapping can diagnose the turbine rotor vibration faults further. The results of diagnosis indicate that the faults diagnosis method based on wavelet packet analysis and neural network has better faults identification capability.
【CateGory Index】: TK268
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