Rolling bearing early fault feature extraction method

Abstract

Disclosed is a rolling bearing early fault feature extraction method. The invention provides a rolling bearing early fault feature method based on sparse optimization, and the method comprises a first step of acquiring acceleration signals of a rolling bearing as signals to be analyzed; a second step of establishing a sparse optimization function of the signals to be analyzed, and solving for period impact components in the signals to be analyzed; and a third step of conducting an envelope demodulation analysis of the period impact components to obtain the fault feature frequency. The invention makes use of the prior knowledge that the rolling bearing fault signals have sparsity, does not need the coefficient sparsity under dictionary transform based on fault signals, and avoids the problem of diagnostic errors caused by the selection of an inappropriate dictionary.

Description

technical field [0001] The invention relates to a fault diagnosis method for rolling bearings, especially a method based on l 1 A feature extraction method for early faults of rolling bearings based on norm minimization algorithm. Background technique [0002] Rolling bearings are one of the most widely used parts in mechanical equipment and one of the wearing parts of rotating machinery. Rolling bearings will always experience the process of normal, early weak faults, serious faults and failure during use. The serious fault stage means that the fault of the rolling bearing has developed to the middle and late stage, and the fault features are obvious and easy to extract; the feature extraction of the early weak fault stage is relatively difficult, because the fault features are weak in the early stage, and the information of other moving parts and the environment interfere It will also be introduced into the bearing system to form background noise, which makes it difficul...

AI Technical Summary

Problems solved by technology

If the dictionary is not properly selected, it will directly affect the results of sparse representation, resulting in inaccurate fault diagnosis results.

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