Intelligent ICA–SVM fault detector for non-Gaussian multivariate process monitoring

Recently, the independent component analysis (ICA) has been widely used for multivariate non-Gaussian process monitoring. For principal component analysis (PCA) based monitoring method, the control limit can be determined by a specific distribution (F distribution) due to the PCA extracted components are assumed to follow multivariate Gaussian distribution. However, the control limit for ICA based monitoring statistic is determined by using kernel density estimation (KDE). It is well known that the KDE is sensitive to the smoothing parameter, and it does not perform well with autocorrelated data. In most cases, the calculated ICA based monitoring statistic is usually autocorrelated. Thus, this study aims to integrate ICA and support vector machine (SVM) in order to develop an intelligent fault detector for non-Gaussian multivariate process monitoring. Simulation study indicates that the proposed method can effectively detect faults when compare to methods of original SVM and PCA based SVM in terms of detection rate.