New kernel independent and principal components analysis-based process monitoring approach with application to hot strip mill process

In this article, a new kernel independent and principal components analysis (kernel ICA-PCA) based process monitoring approach is proposed for hot strip mill process (HSMP). HSMP appears widely in iron and steel industry, which runs in an environment with significant nonlinearity, non-Gaussianity and some other uncertainties. The present method, namely kernel ICA-PCA, firstly addresses the nonlinearity via the popular kernel trick, then applies kernel ICA model to isolate the non-Gaussian independent information, finally, utilises kernel PCA model to account for the uncertain part and extract the principal components. To avoid the disadvantage of the original fault detection statistics, a k nearest neighbour data description-based technique is employed into the kernel ICA-PCA for monitoring the variations occurring in the independent and principal components, whereas traditional Q statistic is employed to reflect the disturbance in the residuals. All of their thresholds will be determined by a new emerging bootstrap-based technique. The applicability of the new scheme is represented via hot strip mill process dataset recorded in the iron and steel company.