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.

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