Application of SVM to engine parameter collector fault diagnosis

Support Vector Machine (SVM), based on structural risk minimization principle, is now widely used in pattern recognition, classification and other research fields. It shows better generalization performance than traditional statistical learning theory, especially in small samples. In this paper, some dimensionless parameter is selected as SVM eigenvector, and then support vector machine is applied to fault diagnosis in engine parameter collector. Result shows that it has good ability in fault pattern classification of engine parameter collector.