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Gear box damage identification model based on ICEEMDAN and IMWPE-LDA-BOA-SVM

Published:2023-11-27 author:WANG Hong, ZHANG Ruili, WU Kai. Browse: 961 Check PDF documents

Gear box damage identification model based on ICEEMDAN and

IMWPE-LDA-BOA-SVM

WANG Hong1, ZHANG Ruili1, WU Kai2

(1.Intelligent Manufacturing College, Yibin Vocational and Technical College, Yibin 644003, China;

2.School of Electromechanical Engineering, Chengdu University of Technology, Chengdu 610059, China)

Abstract: Aiming to address the issue of excessive background noise in gearbox vibration signals affecting the quality of fault features and thereby reducing the accuracy of fault identification, a gearbox fault diagnosis method(ICEEMDAN-IMWPE-LDA-BOA-SVM) based on improved adaptive noise complete ensemble empirical mode decomposition (ICEEMDAN), improved multi-scale weighted permutation entropy (IMWPE), linear discriminant analysis (LDA), butterfly optimization algorithm (BOA), and support vector machine (SVM) optimization was proposed. Firstly, ICEEMDAN was used to decompose the gearbox vibration signal and generate a series of intrinsic mode function components distributed from low frequency to high frequency. Next, based on the correlation coefficient, the intrinsic mode function components containing the main fault information were selected for signal reconstruction to reduce signal noise. Subsequently, a nonlinear dynamic index for improving multiscale weighted permutation entropy was proposed, and it was used to extract fault features of the reconstructed signal to construct fault features that reflect the fault characteristics of the gearbox. Then, linear discriminant analysis (LDA) was used to compress the original fault features to construct a low dimensional fault feature vector. Finally, the butterfly optimization algorithm (BOA)was used to optimize the penalty coefficients and kernel function parameters of the support vector machine(SVM),in order to construct a fault classifier with the optimal parameters, achieving fault identification of the gearbox. Experimental and comparative studies were conducted on the ICEEMDANIMWPEBOASVM method based on the gearbox composite fault dataset. The research results show that the method can accurately identify different fault types of gearboxes, with an accuracy rate of 99.33% and a diagnosis time of only 5.31s. It is superior to other comparative methods in multiple aspects and has more potential for application in fault diagnosis of gearboxes.

Key words: fault feature extraction; signal decomposition and signal reconstruction; feature dimension reduction; improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN); improved multiscale weighted permutation entropy (IMWPE); linear discriminant analysis (LDA); butterfly optimization algorithm (BOA); support vector machine (SVM)

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