Bearing fault diagnosis method based on feature enhancement

Abstract

The invention discloses a bearing fault diagnosis method based on feature enhancement, which can effectively and rapidly extract bearing fault vibration signal shock characteristics while the signal data amount is reduced. Firstly, the bearing fault vibration signals are decomposed by variational mode decomposition (VMD), a kurtosis value and a component with the maximum cross-correlation functionwith the original signal are selected as the optimal components which have better block sparse characteristics. On the basis of the traditional online dictionary learning constraint model, l2, 1 normconstraints of a sparse coefficient are added. Under a new constraint model, sparse representation and dictionary learning are carried out alternatively, inter-block sparse characteristics of the newconstraint can be matched with block sparse characteristics of vibration signals in a sparse representation process, the redundant component in the signals is further removed, l2, 1 norm constraintsare added during the dictionary learning process at the same time, and an experimental result shows that dictionary atoms acquired from the dictionary learning process with new constraints added are more robust against noise interference. The dictionary obtained based on learning and the sparse coefficient are subjected to signal reconstruction, the signal shock characteristics with the redundantcomponents enhanced such as noise in the signals can be removed, and the fault information of the signals is further extracted to complete fault diagnosis.

Description

technical field [0001] The invention relates to a bearing fault diagnosis method based on feature enhancement, in particular to a bearing fault diagnosis method based on variational mode decomposition (VMD) and improved online dictionary learning for bearing fault feature enhancement, which belongs to the technical field of fault diagnosis. Background technique [0002] Bearings are essential components in most rotating equipment that support and guide the rotation of the shaft, and their health directly affects the normal operation of rotating equipment. Since rotating equipment usually operates around the clock in actual production, sensors will collect a large amount of condition monitoring information, and the impact components in these information are often overwhelmed by redundant components such as noise, resulting in redundant and complex data. Therefore, when extracting faults While effectively removing redundant components in the signal and reducing the amount of d...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a bearing fault diagnosis method based on VMD and improved online dictionary learning bearing fault feature enhancement, to solve the technical problems contained in the traditional dictionary learning method in fault diagnosis

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