Reconstruction-Based Contribution for Process Monitoring with Kernel Principal Component Analysis

This paper presents a new method for fault diagnosis based on kernel principal component analysis (KPCA). The proposed method uses reconstruction-based contributions (RBC) to diagnose simple and complex faults in nonlinear principal component models based on KPCA. Similar to linear PCA, a combined index, based on the weighted combination of the Hotel ling's T-2 and SPE indices, is proposed. Control limits for these fault detection indices are proposed using second-order moment approximation. The proposed fault detection and diagnosis scheme is tested with a simulated CSTR process where simple and complex faults are introduced. The simulation results show that the proposed fault detection and diagnosis methods are effective for KPCA.