The Identification Technology of Rolling Bearing Acoustic Emission Fault Pattern based on Redundant lifting Wavelet Packet and SVM

As the energy distribution in each frequency band of rolling bearing acoustic emission (AE) signal is related to its fault type, so we can use the redundant lifting wavelet packet to decompose the rolling bearing AE signal of different fault into different frequency band, combine energy in each frequency band together to be a feature vector of the Support Vector Machines (SVM), then being applied to identify the fault through SVM. This paper also compared the redundant lifting wavelet packet and Daubechies wavelet packet as well as the SVM and neural networks. The experimental result shows that for the fault pattern identification, the method that combines redundant lifting wavelet packet decomposition and SVM together can be effective.