Application of combined support vector machines in process fault diagnosis

The performance of Combined Support Vector Machines, C-SVM, is examined by comparing it's classification results with k-nearest neighbor and simple SVM classifier. For our experiments we use training and testing data obtained from two benchmark industrial processes. The first set is simulated data generated from Tennessee Eastman process simulator and the second set is the data obtained by running experiment on a Three Tank system. Our results show that the C-SVM classifier gives the lowest classification error compared to other methods. However, the complexity and computation time become issues, which depend on the number of faults in the data and the data dimension. We also examined Principal Component Analysis, using PC scores as input features for the classifiers but the performance was not comparable to other classifiers' results. By selecting appropriate number of variables using contribution charts for classification, the performance of the classifiers on Tennessee Eastman data enhances significantly. Therefore, using contribution charts for selecting the most important variables is necessary when the number of variables is large.