Hierarchical adaptive threshold function-based wavelet threshold denoising method

Abstract

The invention discloses a hierarchical adaptive threshold function-based wavelet threshold denoising method. In a process of collecting a bearing signal of a rotary machine, due to interference of field equipment and environment, the collected signal contains noises; and for ensuring real and effective measurement data, it is necessary to perform denoising processing on the collected original bearing signal. The threshold function which is continuous at a threshold, is derivable in a wavelet domain and has a trend parameter is built; by calculating entropy values of noise signal energy and total signal energy of wavelet decomposition layers, the trend parameters of the layers are obtained; and threshold function mathematic models corresponding to the wavelet decomposition layers are obtained. According to the method, the trend parameters are adaptively selected in the decomposition layers through a wavelet threshold function; and noise components in bearing outer ring fault, inner ringfault and ball fault signals can be removed more effectively to achieve a better denoising effect.

Description

technical field [0001] The invention belongs to the technical field of noise reduction of mechanical bearing fault signals, in particular to a wavelet threshold signal-to-noise separation method based on a layered adaptive threshold function for bearing fault signals. Background technique [0002] In the process of collecting bearing signals of rotating machinery, due to the interference of field equipment and the environment, the collected signals contain noise, which will have a greater impact when the equipment is faulty, which is not conducive to fault diagnosis. In order to ensure the authenticity and effectiveness of the measurement data, it is necessary to perform noise reduction processing on the collected original bearing signals. Because wavelet transform has localization characteristics in time domain and frequency domain, its multi-resolution feature is good at dealing with non-stationary signals, and it has achieved very good results in the field of denoising. ...

AI Technical Summary

Problems solved by technology

[0003] In the threshold algorithm, the hard threshold function and soft threshold function are the most common denoising functions, but there are certain limitations. The hard threshold function has discontinuities, and the reconstructed signal will oscillate. The soft threshold signal is also sensitive to low frequency bands. is compressed so that the reconstructed signal is always biased, a function such as figure 1 shown

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