A new kernel trick embedded discriminant model for fault detection and diagnosis

In this paper, a new kernel trick embedded discriminant model is proposed for industrial process fault detection and diagnosis. The proposed method uses kernel trick to improve the discriminant model for fault detection and diagnosis of processes with nonlinear characteristics. The performance of the proposed method is evaluated at a numerical case and Intelligent Process Control-Test Facility. The results show that the proposed method improves the accuracy of fault diagnosis.