Motor bearing fault diagnosis based on multi dimensional features and IGWO SVM

DOI:	10.11809/bqzbgcxb2023.09.019
Keywords:	motor bearing;principal component analysis (PCA);nonlinear convergence factor;Levy flight strategy;improved grey wolf optimization algorithm (IGWO);support vector machine (SVM);fault diagnosis
Abstract:	Aiming at the problems of low fault diagnosis accuracy of motor bearings, the fault diagnosis model optimized by traditional grey wolf optimization algorithm（GWO）to support vector machine(SVM)is prone to fall into local optimality. Nonlinear convergence factor and Levy flight strategy are introduced to study the improved grey wolf optimization algorithm, and a motor bearing fault diagnosis method based on multi dimensional features and improved grey wolf optimization algorithm optimized support vector machine (IGWO SVM) is proposed. The time domain and frequency domain characteristics of motor bearing vibration signals are extracted to form a multidimensional characteristic matrix. The principal component analysis (PCA) is used to reduce the data dimension of feature matrix to realize fast data processing. IGWO is used to optimize the parameters of SVM model, and the optimal IGWO SVM fault diagnosis model is obtained for determining the fault types of motor bearings. Experimental results show that the proposed motor bearing fault diagnosis method has high accuracy and stable performance under different working conditions. Compared with traditional GWO and improved gray wolf optimization algorithm based on differential evolution(DEGWO), the proposed IGWO algorithm has better convergence and accuracy.
Published:	2023-09-28
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