Abstract:

Generally, the vibration signal of the collected motor contains noise components. The wavelet threshold denoising process can achieve good results. However, the traditional soft and hard threshold functions have the disadvantages of constant deviation or discontinuity, and the useful information cannot be well preserved. Aiming at this problem, a new threshold function with adaptive layer number and adjustment parameters was proposed. The improved wavelet threshold function was continuous, differentiable and gradual. The genetic algorithm was used to adjust the parameters and optimize the denoising based on the majority of the original signals, so that the new wavelet denoising algorithm retains useful information and removes noise. There was a good balance between the two. The method was applied to the collected motor vibration signal by experiments. The results showed that the method had higher signal-to-noise ratio and lower root mean square error, which could better filter out noise and retain useful information of the original signal.

Key words: motor vibration signal, wavelet threshold denoising, adaptive, geneticalgorithm, signal to noise ratio, root mean square error