Abstract
For the original acoustic emission signal noise and large amounts of data characteristics, using an improved denoising based on improved lifting Wavelet denoising method. At the same time, combined with BP neural network, is proposed based on the improved lifting wavelet and BP neural network AE signal fault recognition method, the method of AE signal de-noising processing, and then extract the denoising AE waveform signal amplitude and AE parameter signal rise time, duration and average signal level 4 characteristic parameters by the normalized, as network input, through the BP neural network intelligent identification to distinguish between different AE signals. The experimental results show that the method can improve the bearing fault recognition rate of AE signal.
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