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AOA-CEEMDAN and fusion features and its application in gearbox fault diagnosis

Published:2024-05-24 author:MA Weidong, LIU Ziquan, YAO Nan, et al. Browse: 115 Check PDF documents

AOA-CEEMDAN and fusion features and its application in

gearbox fault diagnosis

MA Weidong1,2, LIU Ziquan3, YAO Nan3, ZHU Xueqiong3

(1.College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China; 2.School of Electronic &

Electrical Engineering, Lanzhou Petrochemical University of Vocational Technology, Lanzhou 730087, China;

3.Electric Power Research Institute of State Grid Jiangsu Electric Power Co.,Ltd., Nanjing 210000, China)

Abstract: Aiming at the defect of incomplete signal decomposition caused by artificial setting of parameters of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a gearbox fault diagnosis method based on arithmetic optimization algorithm (AOA) optimized CEEMDAN, fusion features and random forest (RF) was proposed. Firstly, AOA algorithm was used to adaptively select the key parameters of CEEMDAN method, and the optimized CEEMDAN method was used to decompose the gearbox vibration signal to generate several intrinsic mode functions (IMF). Then, the first 4 IMF components were selected as fault sensitive components by correlation coefficient criterion. Next, a fusion feature extraction method composed of attention entropy and diversity entropy was used to mine the fault features of fault sensitive components and obtain fault sensitive feature samples. Finally, the fault features representing the gearbox fault characteristics was input into RF multi-fault classifier to establish a fault classification model and complete the fault identification of the gearbox. Experiments were conducted using the QPZZ-II gearbox dataset, and comparisons were made with other methods. The research results show that the optimized CEEMDAN method can more accurately decompose nonlinear gearbox vibration signals compared to the original CEEMDAN method, and the fault identification accuracy is improved by 4%. Comparing with a single fault feature, fused features can more accurately represent the fault status of the gearbox, and the fault recognition accuracy is respectively improved by 3.2% and 8%. The proposed method provides a feasible idea and scheme for fault feature extraction and fault diagnosis of gearbox.

Key words: gearbox; intrinsic mode functions (IMF); arithmetic optimization algorithm (AOA); complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); random forest (RF)

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