Fault Diagnosis Method of Rolling Bearing Combining VMD and Fastica

Abstract

The invention relates to a rolling bearing fault diagnosis method combining VMD and FastICA. The steps are: using existing data acquisition equipment to collect the original vibration signal of the rolling bearing; performing VMD decomposition on the collected original vibration signal of the rolling bearing; It is decomposed into k modal components, and FastICA analysis is carried out with three continuous modal components as a sequence combination to obtain the reconstructed fault signal; Hilbert transform is performed on the reconstructed fault signal to obtain the envelope spectrum of the reconstructed fault signal. The eigenfrequency, modulation eigenfrequency and sidebands of each fault feature are extracted from the obtained envelope spectrum, and then according to the extracted eigenfrequency components, it is judged whether there is a fault and the type of fault in the rolling bearing.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method, in particular to a rolling bearing fault diagnosis method combining VMD and FastICA. Background technique [0002] Rolling bearings are one of the core components of rotating machinery such as gearboxes and turbomachinery, and their failure will directly affect the stable operation of mechanical equipment. Affected by the working environment, the original vibration signal of the rolling bearing is generally non-stationary and nonlinear, and the signal is often mixed with noise, resulting in a decrease in the signal-to-noise ratio of the vibration signal, which brings difficulties to the fault feature extraction and fault diagnosis of the rolling bearing. Contents of the invention [0003] In view of the above problems, the purpose of the present invention is to provide a rolling bearing fault diagnosis method combining VMD and FastICA, which can effectively reduce the interference noi...

AI Technical Summary

Problems solved by technology

Affected by the working environment, the original vibration signal of the rolling bearing is generally non-stationary and nonlinear, and the signal is often mixed with noise, resulting in a decrease in the signal-to-noise ratio of the vibration signal, which brings difficulties to the fault feature extraction and fault diagnosis of the rolling bearing.

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