Fault diagnosis of automobile engine based on support vector machine

Support vector machine (SVM) based on classification is applied for fault diagnosis of the automotive engine. The basic idea is to identify the information by using the trained SVM model to classify new fault samples. The data from the engine simulation model by AMESim software are fault features extracted, and these fault characteristic parameters have statistical property and specific physical meaning. Principal component analysis (PCA) is used to reduce the dimensions and redundancy of the data, and then these data are normalized as the input of SVM. The proposed method achieves accurate fault classification because SVM has good performance of classification and generalization ability, which is verified by the result of combining MATLAB/SIMULIK and AMESim. And the simulation results indicates that the proposed SVM based fault diagnosis method has achieved the better performance than the Artificial Neural Network, meeting the requirements of real-time diagnosis of the automotive engine.