Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms

Hui Li, Bangji Fan, Rong Jia, Fang Zhai, Liang Bai and Xingqi Luo
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Hui Li: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Bangji Fan: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Rong Jia: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Fang Zhai: School of Humanities and Foreign Languages, Xi’an University of Technology, Xi’an 710048, China
Liang Bai: Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710048, China
Xingqi Luo: Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710048, China

Energies, 2020, vol. 13, issue 6, 1-20

Abstract: Since variational mode decomposition (VMD) was proposed, it has been widely used in condition monitoring and fault diagnosis of mechanical equipment. However, the parameters K and α in the VMD algorithm need to be set before decomposition, which causes VMD to be unable to decompose adaptively and obtain the best result for signal decomposition. Therefore, this paper optimizes the VMD algorithm. On this basis, this paper also proposes a method of multi-domain feature extraction of signals and combines an extreme learning machine (ELM) to realize comprehensive and accurate fault diagnosis. First, VMD is optimized according to the improved grey wolf optimizer; second, the feature vectors of the time, frequency, and time-frequency domains are calculated, which are synthesized after dimensionality reduction; ultimately, the synthesized vectors are input into the ELM for training and classification. The experimental results show that the proposed method can decompose the signal adaptively, which produces the best decomposition parameters and results. Moreover, this method can extract the fault features of the signal more completely to realize accurate fault identification.

Keywords: VMD; grey wolf optimizer; principal components analysis (PCA); multi-domain fault diagnosis; ELM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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