Rolling Bearing Fault Diagnosis Method Based on Improved SSA Optimized SVM
Aiming at the problem of low accuracy of support vector machine classification model in rolling bearing fault diagnosis,a rolling bearing fault diagnosis method based on the improved SSA algorithm optimized support vector machine is proposed.Firstly,the wavelet transform is used to denoise the rolling bearing signals,and the denoised signals are decomposed into wavelet packets to extract the corresponding fault features.Secondly,the sparrow search algorithm is optimized by introducing an improved sea squirt foraging mechanism to prevent the algorithm from converging to the origin,and adaptive Levy flight strategy and elite inversion coefficients are added to enhance the ability of the algorithm to jump out of local optimums.Finally,the improved sparrow algorithm is used to optimize the parameters of the support vector machine to construct a fault diagnosis model with improved SSA-optimized SVM to improve the fault classification effect.Simulation experiments are carried out by applying the bearing dataset provided by Western Reserve University in the USA.The experimental results show that the fault diagnosis effect of the proposed method is better than that of the conventional models such as PSO-SVM,GWO-SVM,SSA-SVM,tSSA-SVM,etc.,and it can effectively extract the fault characteristics of the rolling bearings with high fault diagnosis accuracy.
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