Optimization of Support Vector Machine and Its Application in Intelligent Fault Diagnosis

Single support vector machine has low precision in fault diagnosis of bearing and gear system, the sample feature extraction method of support vector machine and the method of parameter optimization of support vector machine are studied to improve the accuracy of the support vector machine in the fault diagnosis of bearing gear system. The input samples of support vector machines are constructed by the kernel principal component analysis to reduce data redundancy, extract high dimension information of the data, then particle swarm optimization algorithm is used to optimize the kernel function parameter and penalty factor of SVM, finally, the optimized support vector machine model is used for fault diagnosis. A comparative experiment on the fault diagnosis of bearing gear is carried out in order to verify the effectiveness of the proposed method, the results show that the proposed method improves the diagnostic accuracy significantly in comparison with the general support vector machines, the effectiveness and advantages of the intelligent diagnosis method are verified.