Open AccessTransmission Shaft Fault Diagnosis Based on Variational Modal Decomposition (VMD) Feature Fusion
Author(s) -
Zezhong Chong,
Mengqian Zhang,
Sijie Liu,
Yafeng Wu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2224/1/012103
Subject(s) - modal , pattern recognition (psychology) , feature (linguistics) , fault (geology) , artificial intelligence , fusion , vibration , sample entropy , entropy (arrow of time) , feature vector , computer science , singular value decomposition , algorithm , physics , acoustics , materials science , linguistics , philosophy , quantum mechanics , seismology , polymer chemistry , geology
It is difficult to achieve an excellent result to accomplish the transmission shaft fault diagnosis with single feature, so thadt a feature fusion fault diagnosis method is proposed. First, the preprocessed vibration signal is decomposed by variational mode decomposition (VMD), and the Intrinsic mode function (IMF) is obtained; Then, singular value, sample entropy and approximate entropy are obtained as features and fused; Finally, the fused feature is used as the classification object of SVM for fault diagnosis. In this paper, the feasibility of this method is verified by the transmission of experimental data. The experiment included five conditions, and the diagnostic accuracy was 99%.