Feature extraction method for performance degradation evaluation of rolling bearing

Abstract

The invention discloses a feature extraction method for performance degradation evaluation of a rolling bearing. The method comprises the following steps of S1, acquiring vibration signal informationof the rolling bearing; S2, conducting self-adaptive EEMD decomposition on a vibration signal of the rolling bearing; S3, adopting a Bayesian information criterion and a correlation kurtosis method for screening sensitive IMF components, wherein firstly, the Bayesian information criterion is adopted for calculating the number of the sensitive IMF components, secondly, the sensitive components arescreened out according to the values of the correlation kurt (CK), finally, composite spectral analysis is conducted on the sensitive IMF components, and a calculated composite spectral entropy servesas a feature parameter of the performance degradation of the rolling bearing. According to the method, a composite spectral analysis method is adopted for fusing the selected IMF components, the composite spectral entropy is extracted as the degradation feature of the rolling bearing, the sensitivity to the degradation process is high, and the capability of characterizing the degradation processof the rolling bearing by the feature is improved.

Description

technical field [0001] The invention relates to the technical field of rolling bearing performance detection, in particular to a feature extraction method for rolling bearing performance degradation evaluation. Background technique [0002] Rolling bearings are key components of rotating machinery, and their operating conditions determine the performance of the mechanical system. Rolling bearings experience a process from normal to degraded to failure during use. During this process, rolling bearings experience a series of performance degradation states. The performance degradation evaluation of rolling bearings has been explored in recent years, and the signal processing method is used to extract the performance degradation characteristics from the vibration monitoring signal. The spectral analysis method can effectively extract the characteristics of the vibration signal, and has a certain ability to characterize the change process of the fault degree. At present, the com...

AI Technical Summary

Problems solved by technology

The feature extraction of rolling bearing performance degradation evaluation in the prior art should focus on the quantitative reflection ability of features to the degree of rolling bearing performance degradation, rather than distinguishing the differences between different faults, which makes the existing feature extraction method based on the fault characteristic frequency Difficulty adapting to the requirements of performance degradation assessment

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