Rolling bearing fault diagnosis method based on optimized variational mode decomposition

Abstract

The invention provides a rolling bearing fault diagnosis method based on optimized variational mode decomposition, and the method comprises the steps: selecting 4096 sampling points of an original vibration signal as input signals of variational mode decomposition; optimizing the modal number and the secondary penalty factor of variational modal decomposition by adopting an improved bat algorithmand taking the minimum average envelope entropy as an optimization target; decomposing the original vibration signal by using the optimized parameters, and solving an energy entropy and an energy spectrum entropy of a decomposed component; taking the kurtosis, the correlation coefficient and the marginal spectrum entropy as screening criteria to screen the components, and solving main frequency distribution characteristics of the reserved components; and taking the energy entropy, the energy spectrum entropy and the main frequency distribution characteristics as characteristic vectors and inputting the characteristic vectors into a support vector machine so as to realize fault diagnosis. According to the method, the variational mode decomposition parameters are optimized through the improved bat algorithm, and the feature vectors are obtained according to the optimized parameters, so that manual parameter determination is avoided, the optimal solution can be found more quickly, and therecognition rate of the fault state is improved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a rolling bearing fault diagnosis method based on optimized variational mode decomposition. Background technique [0002] Rolling bearings play a vital role in mechanical equipment. Once a fault occurs, it may directly cause abnormalities or even shutdown of mechanical equipment. Research on rolling bearing fault diagnosis methods has become a hot topic in mechanical equipment fault diagnosis in recent years. When a rolling bearing fails, its vibration signal contains a large amount of fault information, but its vibration signal is nonlinear and non-stationary. It is difficult to accurately extract the fault characteristic frequency through traditional spectrum analysis, and it is also impossible to judge the severity of the fault. [0003] Based on the above problems, Huang proposed Empirical Mode Decomposition (EMD, Empirical Mode Decomposition), which is different from...

AI Technical Summary

Problems solved by technology

Although it can effectively avoid modal aliasing and endpoint effects, in the variational modal decomposition, the modal number and the secondary penalty factor need to be manually selected, which is inefficient and if the selection is improper, it will affect the final decomposition effect, thereby affecting Recognition Rate of Fault Diagnosis

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