Online reduced kernel principal component analysis for process monitoring. (January 2018) - Fezai, Radhia; Mansouri, Majdi; Taouali, Okba; Harkat, Mohamed Faouzi; Bouguila, Nasreddine
Highlights: This paper studies the fault detection of nonlinear system using kernel method. An online monitoring method for extracting the reduced number of measurements from the training data was proposed. To evaluate the performance of the proposed method is applied for monitoring a Tennessee Eastman Process. Abstract: Kernel principal component analysis (KPCA), which is a nonlinear extension of principal component analysis (PCA), has gained significant attention as a monitoring method for nonlinear processes. However, KPCA cannot perform well for dynamic systems and when the training data set is large. Therefore, in this paper, an online reduced KPCA algorithm for process monitoring is proposed. The process monitoring performances are studied using two examples: a numerical example and Tennessee Eastman Process (TEP). The simulation results demonstrate the effectiveness of the proposed method when compared to the online KPCA method.