Rolling bearing fault diagnosis method based on conditional empirical wavelet transformation

Abstract

The invention relates to the field of bearing fault diagnosis, discloses a rolling bearing fault diagnosis method based on conditional empirical wavelet transformation, and aims to improve the accuracy of fault feature extraction and fault type identification of a bearing. The method is characterized by comprising the following steps of firstly, acquiring a bearing vibration signal; secondly, setting n frequency spectrum maximum values, and carrying out conditional empirical wavelet transformation on the bearing vibration signal on the basis of the n frequency spectrum maximum values; thirdly,calculating cyclic component ratios of modes after transformation, and storing the maximum values and the modes corresponding to the maximum values; fourthly, if the currently stored maximum cyclic component ratio is larger than a reference cyclic component ratio, defining that n=n+1, taking the currently stored maximum cyclic component ratio as a new reference cyclic component ratio, and repeating the operation; fifthly, selecting out the mode corresponding to the maximum cyclic component ratio stored each time again; and finally, calculating a square envelope spectrum of the selected mode.The method is suitable for the rolling bearing fault diagnosis.

Description

technical field [0001] The invention relates to the field of bearing fault diagnosis, in particular to a rolling bearing fault diagnosis method based on conditional experience wavelet transform. Background technique [0002] Rolling bearings are important components in rotating machinery and play a role in supporting the rotation of rotating parts. When a fault occurs on the bearing, it will not only generate vibration and noise, but also affect the running accuracy of the rotating parts; if the fault cannot be detected in time, it may cause large safety accidents and economic losses. Therefore, early detection of bearing failure is particularly important. According to the structure of the bearing and the location of the fault point, the characteristic frequencies generated when the inner ring, outer ring and rolling element fail can be calculated, which are called the inner ring fault characteristic frequency, outer ring fault characteristic frequency and rolling element f...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a rolling bearing fault diagnosis method based on conditional empirical wavelet transform, which solves the problems of pre-definition of modal numbers, inappropriate spectrum division, and modal selection in empirical wavelet transform, so as to obtain Better bearing fault diagnosis results, and fault diagnosis can be carried out automatically

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