Bearing fault diagnosis method based on cross-correlation singular value

Abstract

The invention, which relates to the technical field of fault signal diagnosis methods for bearings, discloses a bearing fault diagnosis method based on a cross-correlation singular value. The method comprises the following steps that: singular value decomposition is carried out on a bearing vibration signal after phase space reconstruction to obtain singular value component signals of a bearing vibration signal; two singular value component signals are selected to carry out cross correlation, denoising is carried out on the signals and fault characteristics are highlighted based on the characteristics of denoising and same-frequency signal highlighting of the cross-correlation function, so that a cross-correlation time-domain signal is obtained; and envelope demodulation is carried out ona zone with the obvious impact characteristic in a cross-correlation time domain signal and a fault characteristic frequency of the bearing vibration signal is extracted. With the method, a problem ofhigh frequency doubling characteristic missing in the strong noise background is solved; the bearing fault characteristic frequency is extracted accurately; and the high frequency doubling is obtained precisely.

Description

technical field [0001] The invention relates to the technical field of a bearing fault signal diagnosis method, in particular to a bearing fault diagnosis method based on a cross-correlation singular value. Background technique [0002] In recent years, Singular Value Decomposition (SVD) has been widely used in many fields such as data compression, data mining, signal speech coding and digital watermarking, robot motion control, and filter algorithm design. As a nonlinear filtering method, SVD is also a research hotspot in the field of fault diagnosis. SVD processes signals in a high-dimensional space through a matrix, which can effectively filter out noise and extract signal features. Diagnostic research has also achieved a large number of research results. [0003] The problem of effective singular value determination is the hot spot and difficult point of SVD, which determines the signal processing effect of SVD, but this problem has never been well solved. Most of the ...

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Problems solved by technology

Leng Yonggang from Tianjin University used the envelope spectrum to analyze the singular value components, and was able to extract the fault frequency, but the problem of noise interference still existed

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