Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox

Zhijian Wang; Gaofeng He; Wenhua Du; Jie Zhou; Xiaofeng Han; Jingtai Wang; Huihui He; Xiaoming Guo; Junyuan Wang; Yanfei Kou

doi:10.1109/ACCESS.2019.2909300

IEEE Access (Jan 2019)

Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox

Zhijian Wang,
Gaofeng He,
Wenhua Du,
Jie Zhou,
Xiaofeng Han,
Jingtai Wang,
Huihui He,
Xiaoming Guo,
Junyuan Wang,
Yanfei Kou

Affiliations

Zhijian Wang: ORCiD; College of Mechanical Engineering, North University of China, Taiyuan, China
Gaofeng He: College of Mechanical Engineering, North University of China, Taiyuan, China
Wenhua Du: College of Mechanical Engineering, North University of China, Taiyuan, China
Jie Zhou: College of Mechanical Engineering, North University of China, Taiyuan, China
Xiaofeng Han: College of Mechanical Engineering, North University of China, Taiyuan, China
Jingtai Wang: College of Mechanical Engineering, North University of China, Taiyuan, China
Huihui He: College of Mechanical Engineering, North University of China, Taiyuan, China
Xiaoming Guo: College of Mechanical Engineering, North University of China, Taiyuan, China
Junyuan Wang: College of Mechanical Engineering, North University of China, Taiyuan, China
Yanfei Kou: College of Mechanical Engineering, North University of China, Taiyuan, China

DOI: https://doi.org/10.1109/ACCESS.2019.2909300
Journal volume & issue: Vol. 7
pp. 44871 – 44882

Abstract

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The selection of variational mode decomposition (VMD) parameters usually adopts the empirical method, trial-and-error method, or single-objective optimization method. The above-mentioned method cannot achieve the global optimal effect. Therefore, a multi-objective particle swarm optimization (MOPSO) algorithm is proposed to optimize the parameters of VMD, and it is applied to the composite fault diagnosis of the gearbox. The specific steps are: first, symbol dynamic entropy (SDE) can effectively remove background noise, and use state mode probability and state transition to preserve fault information. Power spectral entropy (PSE) reflects the complexity of signal frequency composition. Therefore, the SDE and PSE are selected as fitness functions and then the Pareto frontier optimal solution set is obtained by the MOPSO algorithm. Finally, the optimal combination of VMD parameters (k, a) is obtained by normalization. The improved VMD is used to analyze the simulation signal and gearbox fault signal. The effectiveness of the proposed method is verified by comparing with the ensemble empirical mode decomposition (EEMD).

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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