Bearing signal denoising method based on improved wavelet algorithm

Abstract

The invention relates to a bearing signal denoising method based on an improved wavelet algorithm, and belongs to the technical field of fault detection of mechanical and electrical equipment bearings. The technical scheme comprises the following step of firstly performing wavelet transform on a noisy signal to obtain a wavelet decomposition coefficient; obtaining an estimated wavelet decomposition coefficient by selecting appropriate threshold and threshold function for appropriate threshold and threshold function for processing; and reconstructing the estimated wavelet decomposition coefficient to obtain a denoising signal. The bearing signal denoising method based on the improved wavelet algorithm provided by the invention uses an improved progressive method to carry out denoising research on the wavelet threshold and the wavelet threshold function. From the selection of the traditional threshold function, the improved threshold function, and the traditional threshold and the improved threshold, the combination of the traditional threshold function with the improved threshold has the best denoising effect, the combination of the traditional threshold function with the improved threshold has the secondary denoising effect, and the combination of the traditional threshold function with the traditional threshold has the worst denoising effect. In the wavelet denoising algorithm, the improved wavelet threshold method can obtain good denoising effect.

Description

technical field [0001] The invention relates to a bearing signal noise reduction method based on an improved wavelet algorithm, and belongs to the technical field of fault detection of electromechanical equipment bearings. Background technique [0002] With the continuous advancement of science and technology and the continuous advancement of industrialization, the normal operation of large rotating machinery and equipment plays a pivotal role in metal smelting, petrochemical, power system, textile machinery, aerospace and other industries. The requirements for real-time, fast and accurate fault diagnosis of large rotating machinery are also getting higher and higher. For example, in a large-scale thermal power plant, once some important mechanical bearings fail, it will cause certain hidden dangers to the stable operation of the rotating machinery, and even cause a series of catastrophic disasters such as damage to machinery and equipment, personal safety accidents, etc. a...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a bearing signal noise reduction method based on the improved wavelet algorithm to solve the problems of wavelet threshold function selection and wavelet threshold selection. The innovation of the noise reduction method is reflected in the noise reduction effect. The root mean square error RMSE and The signal-to-noise ratio (SNR) is used as the evaluation standard. In the wavelet denoising algorithm, the improved wavelet threshold method can obtain a better denoising effect and solve the above-mentioned technical problems existing in the existing technology.

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