Bearing fault diagnosis method and system for optimizing filtering MCKD

Through the combination of Fourier transform and Mayer wavelet filter, the problem that the fault diagnosis results in the prior art are difficult to determine the global optimal solution, and efficient and accurate diagnosis of bearing fault signals is achieved, and the reliability and accuracy of fault diagnosis are improved.

CN119669738AActive Publication Date: 2025-03-21BEIJING UNIV OF TECH
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Patent Information

Application Number
CN202411776628.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-05
Publication Date
2025-03-21
Estimated Expiration
2044-12-05

AI Technical Summary

Technical Problem

When the existing blind deconvolution method processes strong noise signals or weak fault signals, it is difficult to determine whether the result is a global optimal solution and it is easy to converge to the local optimal solution.

Method used

The spectrum segmentation boundary is determined through Fourier transform and inverse transform and the filtering band is divided. The Mayer wavelet filter is designed as the initial filter coefficient of MCKD, and the fault characteristics are quantified by harmonic spectral metrics, and the global optimal solution is selected.

Benefits of technology

It realizes efficient and accurate diagnosis of fault signals, improves the reliability and accuracy of fault diagnosis, and can quickly locate the root cause of equipment failures, reducing equipment maintenance costs and downtime.

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Abstract

The embodiment of the invention provides a bearing fault diagnosis method and system for optimizing filtering MCKD, and the method comprises the steps: collecting a vibration signal, and carrying out the Fourier transform of a frequency spectrum of the vibration signal, and obtaining a key function; setting an initial interception length, performing inverse Fourier transform on the key function based on the initial interception length to obtain a frequency spectrum trend, dividing filtering frequency bands with different bandwidths and center frequencies by taking a minimum value of a trend spectrum as a boundary of frequency spectrum segmentation, increasing the interception length, repeating the steps, and recording all results; designing a Mayer wavelet filter based on the obtained filtering frequency band, and taking a designed Mayer wavelet filter coefficient as an initial filter coefficient of MCKD; mCKD filtering results of different initial filter coefficients are calculated, the fault characteristics of all filtering signals are quantized through harmonic spectrum kurtosis HSK, the filtering result corresponding to the maximum HSK value is selected as the global optimal solution of MCKD, and fault diagnosis is completed.
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