SVM based on principal component augmented matrix for fault detection

To effectively improve the fault detection rate of the support vector machine (SVM) based method, we propose a fault detection method with SVM based on principal component augmented matrix (PCAM-SVM). Firstly, the principal component analysis (PCA) algorithm is used to obtain the scores of training data in principal component space. Secondly, the input characteristics of time delay and time difference for the scores are added to construct the augmented matrix. Then, the combined augmented matrix on normal data and fault data is used to obtain the discriminant function of SVM model for classification. Finally, the SVM model is used to perform the classification operation on test data. The proposed method increases the complexity of input characteristics of model, reduces data autocorrelation, and enhances the fault detection performance of SVM by constructing principal component augmented matrix. The method has been applied to a multivariate dynamic simulation case and the Tennessee-Eastman process. The simulation results validate the feasibility and effectiveness of PCAM-SVM by comparison with PCA, independent component analysis (ICA), kernel principal component analysis (KPCA), SVM and PCA-SVM.