Weighted sliding window second-order synchronous compression S transformation bearing fault diagnosis method

Abstract

The invention relates to the field of bearing fault diagnosis, and discloses a weighted sliding window second-order synchronous compression S transformation bearing fault diagnosis method, which is used for improving the precision of bearing fault feature extraction and fault type identification. According to the invention, an optimal weighted sliding window module is constructed according to bearing fault vibration signal characteristics; second-order synchronous compression S transformation is fused into a plurality of step modules, a Gaussian window best matched with a fault feature signalis obtained in a self-adaptive mode in the signal time-frequency analysis process through an optimization algorithm, the weight is calculated through the obtained time-frequency analysis Renyi entropy, and then overall signal reconstruction output is achieved. The bearing fault characteristics are accurately extracted and the fault type is accurately identified through the time-frequency spectrogram, and the method is suitable for rolling bearing fault diagnosis.

Description

technical field [0001] The invention relates to the field of bearing fault diagnosis, in particular to a weighted sliding window second-order synchronous compression S-transformation bearing fault diagnosis method. Background technique [0002] As a key mechanical component of rotating machinery, rolling bearings are widely used in various industrial fields. Its operating status monitoring and fault diagnosis are of great significance to ensure equipment reliability and avoid safety accidents. However, in actual engineering, since the bearing fault characteristic information is often overwhelmed by strong background noise and other unstable components, the extraction of bearing fault characteristic information has become a difficult task. [0003] The time-frequency analysis of signals can describe the frequency information of each fault component changing with time. Common signal time-frequency analysis methods, such as short-time Fourier transform, wavelet transform, and S...

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Problems solved by technology

[0004] At present, the traditional spectrum analysis method is not effective in the extraction of bearing fault features and the accurate identification of fault types, and it is increasingly difficult to meet today's production requirements.

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