Automatic warning method of rolling bearing state based on multiple feature extraction and selection

Abstract

The invention discloses a rolling bearing state automatic early warning method based on extraction and selection of multiple characteristics. The method comprises the steps of extracting multiple time domain, frequency domain and time-frequency domain characteristics of a vibration signal of a rolling bearing, and intelligently selecting a characteristics subset which is sensitive to the fatigue recession process of the rolling bearing and can supply complementary information by using a supervision characteristic-free selection method based on maximum correlation and minimum redundancy. The blindness of manually selecting sensitive characteristics without priori knowledge is overcome, an alarm policy is set up according to an alarm threshold value automatic establishing method, and the early damage warning of the rolling bearing is realized.

Description

technical field

[0001] The invention relates to the field of mechanical fault diagnosis, in particular to an automatic early warning method for a rolling bearing state. Background technique

[0002] The state evaluation technology of rolling bearings based on vibration signals generally directly selects a single feature of the vibration signal as the state evaluation index. However, with further research, it is found that a single feature is usually only effective for a certain defect at a certain stage, the same feature behaves differently under different failure modes, different features behave differently under the same working conditions, and effective state evaluation indicators It should be able to make full use of a variety of information, which can not only capture the inherent performance changes of bearings in different operating stages, but also be easy to obtain in practical applications. In order to improve the performance of a single indicator, some scholars h...

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