Study of fault diagnosis method based on ensemble-multi-SVM classifiers

In order to improve the system accuracy of fault diagnosis, this paper proposes the integrated fault diagnosis method based on multi-SVM classifiers. MultiBoost integrated learning method using the AdaBoost algorithm and Wagging algorithm composed of multiple integrated with a combination of base classifiers to improve the classification accuracy of the system. The simulation results show that the method used in network fault diagnosis system of classification module design, making fault diagnosis accuracy has been significantly improved.