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Fault diagnosis method for bearing clearance of reciprocating compressor based on DE-VMD and GMDE

Published:2024-04-24 author:LI Yanyang, CAI Jianhua, QU Xiaohai. Browse: 1135 Check PDF documents

Fault diagnosis method for bearing clearance of reciprocating compressor

based on DE-VMD and GMDE

LI Yanyang1,2, CAI Jianhua3, QU Xiaohai3

(1.College of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China; 2.College of Civil

Engineering and Water Conservancy Institute, Heilongjiang Bayi Agricultural University, Daqing 163319, China; 3.College of

Mathematics and Science, Hunan University of Arts and Sciences, Changde 415000, China)

Abstract: Aiming at the difficulty of feature extraction and low recognition accuracy of bearing clearance fault of reciprocating compressor, a fault diagnosis method of bearing clearance fault of reciprocating compressor was proposed by combining differential evolution (DE) algorithm optimization variational mode decomposition(VMD) method and generalized multi-scale dispersal entropy. Firstly, the differential evolution algorithm was used to optimize the two core parameters of the variational mode decomposition algorithm, and the optimized variational mode decomposition method was used to decompose and reconstruct the vibration signal of the bearing clearance. Then, the coarse-grained process of the multi-scale dispersal entropy algorithm(MDE) was studied, and the generalized multi-scale dispersal entropy algorithm(GMDE) was constructed by using variance coarse-grained instead of mean coarse-grained to carry out multi-scale processing. Finally, the kernel extreme learning machine model(KELM) was designed to classify and identify the fault feature vector set, and the intelligent diagnosis of different fault states of the bearing clearance of reciprocating compressor was completed. The research results show that the identification accuracy of this fault diagnosis method is as high as 97%, and the intelligent diagnosis of different kinds of bearing faults is realized efficiently.

Key words: reciprocating compressor; bearing fault diagnosis; variational mode decomposition(VMD); generalized multi-scale dispersion entropy(GMDE); kernel extreme learning machine(KELM); differential evolution (DE) algorithm

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