Fault diagnosis method of rolling bearing under variable working conditions

Abstract

The invention relates to a fault diagnosis method of a rolling bearing under variable working conditions. The method comprises the steps of: extracting a characteristic vector of a signal by means of Hilbert-Huang conversion, carrying out EMD (Empirical Mode Decomposition) to a signal to obtain a plurality of IMFs (Intrinsic Mode Function), and selecting useful IMFs to carry out Hilbert conversion respectively to obtain analytic signals Hi(t) and taking envelopes respectively to form a characteristic vector w; then, carrying out singular value decomposition for w by a singular value decomposition method, and using a singular value matrix as the final fault characteristic vector; using the singular value matrixes in four modes of the rolling bearing as input and four matrixes corresponding to the four modes as output to train an Elman neural network. When the rolling bearing fails, whether the rolling bearing is faulted or not can be judged and direction of faults can be accurately positioned through analysis and judgment of a signal at t moment by the trained Elman neural network. According to the invention, classification of fault modes of the rolling bearing under variable working conditions can be effectively finished, and higher precision is maintained.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of rolling bearings, and in particular relates to a fault diagnosis method of rolling bearings under variable working conditions based on Hilbert-Huang transformation and singular value decomposition. Background technique [0002] The role of rolling bearings is to support the rotating shaft and the parts on the shaft, and maintain the normal working position and rotation accuracy of the shaft. It is characterized by convenient use and maintenance, reliable operation, good starting performance, and high bearing capacity at medium speeds. Rolling bearings are key components commonly used in mechanical equipment. Whether their working status is normal or not is directly related to the production quality and safety of the entire unit and even the entire production line. Compared with other mechanical parts, rolling bearings have a prominent feature: their life span is highly discrete, that is...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the defect that the commonly used rolling bearing fault diagnosis method usually fails or its judgment accuracy is greatly reduced when the working condition changes

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