Fault pattern recognition technique for roller bearing acoustic emission based on harmonic wavelet packet and BP neural network

作者：Zhao Yuan Xi^*; Xu Yong Gang; Gao Li Xin; Cui Ling Li

来源：Journal of Vibration and Shock, 2010, 29(10): 162-165.

• Acoustic emission signal
• AE signals
• Back propagation neural networks
• Bearing defect
• Bearing fault recognition
• Bearing tests
• BP neural networks
• Daubechies Wavelet
• Energy distributions
• Energy feature
• Fault diagnosis method
• Fault patterns
• Harmonic wavelet
• Roller bearing fault
• Sub-bands

The energy distribution of each frequency sub-band of acoustic emission(AE) signal after decomposition is related to the type of roller bearing defects, AE signals measured from a roller bearing test rig were decomposed into a number of frequency sub-bands by using harmonic wavelet packet, and energy features associated with each sub-band were selected. The energy features were then used as inputs to a back-propagation neural network classifiers for identifying the bearing';s fault. In the bearing fault recognition, harmonic wavelet packet was compared with Daubechies wavelet packet. The experimental results indicated that the proposed fault diagnosis method is effective and can be used for roller bearing fault recognition.

• 出版日期2010
• 单位北京大学