Design of fuzzy SVM multi-category classifier model and application in engine fault diagnosis

Support Vector Machines (SVM) is a new-generation machine learning technique based on the statistical learning theory. They can solve small-sample learning problems better by using Structural Risk Minimization in place of Experiential Risk Minimization. It can solve the problem of small sample sets learning and avoid the problem of over-learning with limited swatch amount at the same time. A fuzzy SVM multi-category classifier system model based on “one-against-all” is designed and established, which improves the performance of SVM and classification precision by reducing the blind area with fuzzy theory. It has good learning ability and generalization performance by the experiment with RBF-NN, Common-SVM and Fuzzy-SVM. At last, this model is applied in engine fault diagnosis, which improves classification accuracy and satisfies with the request of fault diagnosis for the engine.