Rolling bearing weak fault diagnosis method based on cyclic correlation entropy and low-rank sparse model

Through a method based on cyclic correlation entropy and low-rank sparse model, the fault diagnosis problem of cyclostationary signal processing under non-Gaussian noise is solved, and effective fault feature extraction is achieved in the case of a mixture of impulse noise and strong Gaussian noise, achieving the accuracy of fault features and diagnostic precision.

CN120628609APending Publication Date: 2025-09-12ANHUI UNIV +1
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Patent Information

Application Number
CN202510750644.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-06
Publication Date
2025-09-12

AI Technical Summary

Technical Problem

Traditional cyclostationary signal processing technology is not effective when processing non-Gaussian noise, especially when the fault diagnosis method fails under impulse noise interference, and the cyclic correlation entropy spectrum is submerged in the presence of strong Gaussian noise and impulse mixed noise.

Method used

A sparse model based on cyclic correlation entropy and low-rank sparsity is adopted. The Gaussian kernel length is determined by the Silverman criterion, the time-varying correlation entropy is calculated and Fourier series transform is performed, and the truncated nuclear norm is used for low-rank sparse decomposition to extract the fault characteristic frequency and identify the fault type.

Benefits of technology

In the case of impulse noise and strong Gaussian mixed noise, the accuracy of fault feature extraction is improved, high diagnostic accuracy is maintained, and the fault feature frequency can be effectively extracted.

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Abstract

The invention discloses a rolling bearing weak fault diagnosis method based on cyclic correlation entropy and a low-rank sparse model, and belongs to the field of bearing fault diagnosis, and the method comprises the following steps: collecting a vibration signal of a rolling bearing, and determining a Gaussian kernel length based on the vibration signal by adopting a Czofmann criterion; based on the Gaussian kernel length, calculating a time-varying correlation entropy of the vibration signal by using a Gaussian kernel function; fourier series transformation is carried out on the time-varying correlation entropy to obtain a cyclic correlation entropy; fourier transform is carried out on the cyclic correlation entropy to obtain a cyclic correlation entropy spectrum; performing low-rank sparse decomposition on the cyclic correlation entropy spectrum by using a truncated kernel norm to obtain a sparse matrix; and solving an enhanced envelope spectrum for the sparse matrix, extracting a fault characteristic frequency based on the enhanced envelope spectrum, and judging a fault type based on the fault characteristic frequency.
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