Bearing fault classification diagnosis method based on sparse representation and LDM (large margin distribution machine)

Abstract

The invention provides a bearing fault classification diagnosis method based on sparse representation and an LDM (large margin distribution machine), overcomes the defects that signal decomposition is incomplete, a reconstructed signal cannot better keep features of an observed signal and the like in the conventional single-channel mechanical compound fault diagnosis method. According to the method, signal conversion from one dimension to high dimension is realized with a CEEMD (complete ensemble empirical mode decomposition) method, the decomposition completeness is guaranteed, and a mode mixing phenomenon is inhibited; meanwhile, a dimensionality reduction method based on sparse representation is introduced into a feature extracting and processing process of a blind source signal, data are subjected to sparse reconstruction through sparse representation, and data feature information is extracted from global data, so that the reconstructed signal can better keep the data features of the observed signal; further, the LDM classification method is introduced into a model fault type classification processing process of a to-be-detected bearing, and the accuracy and effectiveness of bearing fault diagnosis can be improved by aid of the generalization ability of the LDM classification method.

Description

technical field [0001] The invention belongs to the technical field of mechanical fault diagnosis and computer artificial intelligence, and in particular relates to a bearing fault classification and diagnosis method based on sparse representation and large interval distribution learning. Background technique [0002] Blind signal processing technology is a new technology developed in the late 1980s, which has excellent capabilities of blind separation, blind identification and feature extraction. Its main idea is: when the prior information of the source signal is scarce and the parameters of the transmission channel are unknown, the source signal and the parameters of the transmission channel can be estimated only by observing the signal. In recent years, the application of blind source separation has gradually extended to the mechanical field, which provides a new method for the research of fault diagnosis. However, the blind source separation method currently applied to...

AI Technical Summary

Problems solved by technology

[0009] Aiming at the above-mentioned problems existing in the prior art, in order to overcome the problems existing in the existing mechanical fault diagnosis methods, such as incomplete signal decomposition, difficulty in maintaining the characteristics of observed signals, and low accuracy of fault diagnosis, the present invention provides a A bearing fault classification and diagnosis method based on sparse representation and large-interval distribution learning. This method realizes the transformation of signals from one-dimensional to high-dimensional through the complete overall empirical mode decomposition method, which ensures the completeness of the decomposition and suppresses the modal aliasing phenomenon. , to improve the accuracy and effectiveness of bearing fault diagnosis

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