One-class support vector machine based on dynamic independent component and its application to fault diagnosis

Key words:  one-class support vector machine  dynamic independent component analysis  fault detection  fault identification

Abstract:

In order to analyze dynamic, nou-Gaussian and nonlinear property of data in industrial process fault diagnosis, one-class support vector machine based on dynamic independent component was presented. Dynamic independent component analysis was firstly applied to deal with dynamic and non-Gaussian data to obtain dynamic independent components as feature information. Then one-class support vector machine was used to build nonlinear monitoring statistics based on feature information. After fault was detected, the similarity between new fault data and fault pattern data was computed for fault pattern identification according to their decision hyper planes. The simulation results on Tennessee Eastman benchmark process show that the proposed method can detect fault more effectively than one-class support vector machine and detect diagnosis fault pattern correctly.