Bearing Fault Feature Extraction Method Based on Variational Mode Decomposition of Fractional Fourier Transform
Source abstract
Abstract
A new method of fault feature extraction based on the fractional fourier transform variational mode decomposition (FRFT-VMD) method is proposed. First, the core idea of this method is to perform the optimal fractional fourier transform (FRFT) on the original signal. And then the transformed signal is subjected to variational mode decomposition (VMD). Aiming at the problem that the order of FRFT is difficult to determine, a fourth-order central moment (FOCM) method is proposed to determine the optimal order. And use the kurtosis standard deviation criterion (KSDC) to optimize the parameters of VMD. So that FRFT-VMD can be optimized. Finally calculating the kurtosis and impulse factor of the decomposed signal, so as to realize the extraction of fault characteristics. The research results of experimental data show that the signals extracted by this method contain more and more obvious fault characteristic frequencies, which greatly improves the accuracy of fault diagnosis in different states of the bearing normal state, inner ring fault, ball body fault, outer ring fault, etc.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Engineering Diagnostics and Reliability · Gear and Bearing Dynamics Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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