Improving new wavelet threshold by artificial fish swarm and its application for denoising

Abstract

Vibration signal is the main measurement signal of mechanical component fault diagnosis, and the presence of noise affects the feature extraction of the signal and the final fault diagnosis, so the test signal in practice needs to be denoised. The use of wavelet transform does not filter out the noise in the signal very well. This is because the hard threshold function is not continuous and some useful information is filtered out. There is a deviation in the soft threshold function and the noise in the signal cannot be completely filtered out. And the traditional threshold is fixed. In order to solve the problem of threshold function and threshold, this paper proposes a new threshold function, and uses artificial fish swarm algorithm to get the optimal threshold. Finally, the superiority and practicability of the method are verified by the unsteady test signal and the bearing dataset of Case Western Reserve University. From the final noise reduction result, the method can achieve better performance in noise reduction than other existing methods. The signal-to-noise ratio obtained by this method is 13%∼16% higher than other methods. The root mean square error is 10%∼41% lower than other methods.