Method for extracting rolling bearing fault features under strong interference

Abstract

The invention discloses a method for extracting rolling bearing fault features under strong interference and belongs to the technical field of fault diagnosis and signal processing analysis. A vibration sensor is used for collecting acceleration vibration signals of a rolling bearing; a VDM is used for decomposing the acceleration vibration signal into a series of IMF components; a fourth moment of a mean square envelope function of each IMF component and a fourth moment after narrowband filtering of each IMF component are calculated, a kurtosis rate of each IMF component is calculated according to the result, and finally an IMF component reconstruction signal with a relatively high kurtosis rate is selected, and fault features of the rolling bearing are extracted by utilizing mean squareenvelope analysis; the invention provides a method for extracting rolling bearing fault characteristics when strong interference exists in background noise, and an effective diagnosis tool is providedfor realizing a self-diagnosis function of an intelligent bearing.

Description

technical field [0001] The invention belongs to the field of fault diagnosis technology and signal processing and analysis technology, and particularly relates to a method for extracting fault features of rolling bearings under strong interference. Background technique [0002] Rolling bearings are one of the important supporting components of rotating machinery, and the normal operation of the equipment is closely related to its operating status. The method of extracting fault features to diagnose rolling bearing faults by analyzing vibration signals is widely used. However, when the vibration signal collected under actual working conditions is subject to strong interference, the fault signal is easily overwhelmed by background noise and difficult to extract. In order to weaken the influence of strong interference in the background noise, it is necessary to reduce the noise of the original signal first and then improve the fault characteristics. [0003] Variational mode ...

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Problems solved by technology

[0004] In order to overcome the defects of the above-mentioned prior art, the object of the present invention is to provide a method for extracting rolling bearing fault features under strong interference, by using VMD to decompose the original signal into a series of IMF components, and then calculate the mean square envelope of each IMF component The fourth-order moment of the function and its narrow-band filtered fourth-order moment, and then obtain the kurtosis rate of each IMF component according to the above results, and finally select the IMF component with a larger kurtosis rate to reconstruct the signal, and use the mean square envelope analysis Extract the useful information in the reconstructed signal, and solve the problem that it is difficult to extract the fault features of rolling bearings under strong interference

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