A new data-driven process monitoring scheme for key performance indictors with application to hot strip mill process

Hot strip mill process (HSMP) plays a pivotal role in steel manufacturing industry, but involves significant complexity. Several faults could cause the decreasing evaluation of the key performance indicators (KPIs). Partial least squares (PLS) model has been popularly accepted for KPI-monitoring tasks, whereas some drawbacks have been reported such as high false alarm rate and strict limitation of Gaussian distribution. In this paper, a new scheme is designed without any distributional priority. The process information is extracted by the independent component analysis (ICA) and principal component analysis (PCA) one after another to obtain the Non-Gaussianity and Gaussianity rooted in process variables. Then the correlation canonical analysis (CCA), a classic tool of analyzing the correlation of two data sets, will be utilized to incorporate the process information and KPIs. Finally, two KPI-related indices are formed respectively, which are both bounded by key density estimation based approach. In the end, application of the new approach in a real steel plant will be demonstrated, where the comparison with PLS based results is covered.