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Bearing fault feature extraction method based on unilateral attenuation wavelet convolution sparse

An extraction method and a technology of fault characteristics, applied in the field of fault diagnosis of rolling bearings, can solve the problems of inability to realize timely and accurate diagnosis of bearing faults, noisy operating environment, and redundant component masking, etc., and achieve obvious characteristics of periodic pulse components and fault characteristics The effect of frequency enhancement

Pending Publication Date: 2022-07-22
BEIJING UNIV OF CHEM TECH
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Problems solved by technology

However, due to the noisy operating environment of the equipment, this impact component is often covered by redundant components, so that timely and accurate diagnosis of bearing faults cannot be realized

Method used

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  • Bearing fault feature extraction method based on unilateral attenuation wavelet convolution sparse
  • Bearing fault feature extraction method based on unilateral attenuation wavelet convolution sparse
  • Bearing fault feature extraction method based on unilateral attenuation wavelet convolution sparse

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Embodiment Construction

[0032] The method includes the following specific steps:

[0033] Step 1: Use the signal of the bearing collected by the acceleration sensor, denoted by S, which is the one-dimensional vector of the bearing fault used for analysis. Through time domain analysis and envelope spectrum analysis, such as Figure 5 As shown, the type of bearing failure cannot be accurately identified.

[0034] Step 2: Analyze the signal S through the sparse bearing fault feature extraction method based on unilateral attenuation wavelet convolution proposed by the present invention. The fault frequency of the vibration signal is analyzed by short-time Fourier transform, and the core fault frequency band f is confirmed i . For core high frequency f mi is the maximum value of the frequency band, which can be directly determined by the Fourier time-frequency diagram, such as image 3 shown. The core band is f mi It is a narrow-area contraction interval in the center, and the interval length is 40...

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Abstract

The invention discloses a bearing fault feature extraction method based on unilateral attenuation wavelet convolution sparsity, belongs to the field of rotating machinery fault diagnosis, and relates to a fault diagnosis method of a rolling bearing. According to the method, a unilateral attenuation wavelet most similar to a shock waveform is constructed through intrinsic analysis of a collected fault vibration signal and correlation analysis in a given domain. The method particularly relates to unilateral attenuation wavelet and vibration signal convolution noise reduction, reduces redundant components of the signals, realizes extraction of main impact characteristics of the signals, and realizes fault diagnosis of the bearing.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of rotating machinery, and relates to a fault diagnosis method of a rolling bearing. This invention constructs the unilateral attenuation wavelet which is most similar to the shock waveform through the intrinsic analysis of the collected fault vibration signal and the correlation analysis in a given domain. In particular, it involves the convolution and noise reduction of unilateral attenuation wavelet and vibration signal, which reduces the redundant components of the signal, realizes the extraction of the main impact characteristics of the signal, and realizes the fault diagnosis of the bearing. Background technique [0002] Failure prediction and health management technology (PHM, Prognostics Health Management) is mainly through the state perception, health monitoring, data analysis, and failure prediction of mechanical equipment or components, so as to improve the operation efficiency of equipme...

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Application Information

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IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 王华庆韩长坤宋浏阳
Owner BEIJING UNIV OF CHEM TECH
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