Non-linear process monitoring using kernel principal component analysis: A review of the basic and modified techniques with industrial applications

Timely detection and diagnosis of process abnormality in industries is crucial for minimizing downtime and maximizing profit. Among various process monitoring and fault detection techniques, principal component analysis (PCA) and its different variants are probably the ones with maximum applications. Because of the linearity constraint of the conventional PCA, some non-linear variants of PCA have been proposed. Among different non-linear variants of PCA, the kernel PCA (KPCA) has gained maximum attention in the field of industrial fault detection. This article revisits the basic KPCA algorithm along with different limitations of KPCA and the crucial open issues in design of KPCA based monitoring system. Different modifications proposed by different researchers are reviewed. Strategies adopted by various researchers for optimal selection of kernel parameter and number of principal components are also presented.