Journal
RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING
Volume 58, Issue 3, Pages 221-235Publisher
PLEIADES PUBLISHING INC
DOI: 10.1134/S1061830922030056
Keywords
fractional Fourier transform; variational mode decomposition; feature extraction; Fault diagnosis
Funding
- State Key Laboratory of Petroleum Resources and Exploration of China University of Petroleum (Beijing) [PRP/open-1610]
- National Natural Science Foundation of China [51804267]
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A new method of fault feature extraction based on the FRFT-VMD method is proposed, which optimizes the FRFT and VMD for fault feature extraction and improves the accuracy of bearing fault diagnosis.
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.
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