Fault diagnosis method of mechanical parts based on svd and vmd

Abstract

The invention relates to a fault diagnosis method for mechanical parts based on SVD and VMD. The differential spectrum is obtained by the difference between adjacent singular values; the differential spectrum is analyzed to determine the reconstruction order, and the vibration signal of the mechanical part is reconstructed; the reconstructed signal is decomposed into modal components to determine the optimal number of modal components; Carry out variational mode decomposition on the vibration signal of the mechanical parts of the original fault diagnosis; select the modal component with a large weighted kurtosis index value for signal synthesis; use the synthesized signal to generate an envelope spectrum; judge the mechanical parts according to the envelope spectrum Fault type. The invention solves the problem that the decomposition parameters of the variational mode decomposition method are difficult to determine, and considers the correlation between the mode components and the original signal in the process of determining the optimal number of mode components through the variational mode decomposition of the reconstructed signal The definite decomposition parameters are better.

Description

technical field [0001] The invention belongs to the field of mechanical fault diagnosis, in particular to a fault diagnosis method for mechanical parts based on SVD and VMD. Background technique [0002] Once key mechanical equipment fails, it will bring huge economic losses and even casualties. Major accidents caused by mechanical equipment failures have occurred frequently at home and abroad. It is of great significance for the safe operation of equipment and the avoidance of economic losses and catastrophic accidents to find fault symptoms and take measures in time during the operation of equipment. [0003] One of the difficulties in mechanical fault diagnosis is to extract useful fault feature information from complex monitoring signals, which has attracted extensive attention in industry and academia in recent years. Variational mode decomposition (VMD) is a signal adaptive decomposition method proposed by UCLA scholars Dragomiretskiy and Zosso in 2014. As an improved...

AI Technical Summary

Problems solved by technology

However, VMD decomposition parameters such as the number of modal components and frequency bandwidth control parameters of modal components have a significant impact on its decomposition results

At present, in the field of mechanical failure, the VMD decomposition parameters are specified in advance in most studies, and it is difficult to obtain satisfactory analysis results.

Although a few scholars have proposed to use the envelope entropy as a fitness function to optimize the VMD decomposition parameters, the adopted envelope entropy only considers the characteristics of the decomposed modal components, and does not consider the correlation between the modal components and the original signal, which is easy to cause missing information problem

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