Open AccessFault Diagnosis of Composite Features Rolling Bearing Based on Variational Mode Decomposition
Author(s) -
Yi Lin Yuan,
Min Zhang,
Xiaojun Li
Publication year - 2020
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 - 10.1088/1742-6596/1650/2/022073
Subject(s) - particle swarm optimization , fault (geology) , feature extraction , pattern recognition (psychology) , modal , feature (linguistics) , signal (programming language) , time domain , bearing (navigation) , support vector machine , artificial intelligence , computer science , feature vector , vibration , algorithm , engineering , acoustics , computer vision , linguistics , chemistry , programming language , philosophy , physics , seismology , polymer chemistry , geology
In order to extract the vibration signal feature of rolling bearing with non-steady feature and improve the fault diagnosis rate accurately and stably, a variational mode decomposition (VMD) feature extraction method is proposed. Particle swarm optimization (PSO) is used to optimize the parameters of support vector machine to construct a fault diagnosis model to achieve fault diagnosis of rolling bearings. Firstly, change the modal decomposition of the known fault signal under the same load to get the modal function, and the modal function is further extracted by the singular value decomposition. The time domain, frequency domain feature and modal feature of the original signal are extracted. Constructing hybrid features to achieve efficient fault feature extraction. Optimizing SVM parameters through PSO algorithm to construct fault diagnosis models to achieve efficient fault diagnosis. Finally, by comparing with EMD-based feature extraction methods in the same load, the method shows better classification performance and the overall fault recognition rate remains above 99.17%, which verifies the reliability and effectiveness of the method.