A novel dynamic non-Gaussian approach for quality-related fault diagnosis with application to the hot strip mill process

This paper addresses the dynamic non-Gaussian, quality-related fault diagnosis problem for process industries, which is driven by the fact that the quality indices of the industrial products, such as the thickness and flatness in the hot strip mill process (HSMP), are increasingly emphasized. Traditionally, partial least squares (PLS)-based methods are extensively used for quality-related fault diagnosis, however, they are preferred for the static processes. In this paper, a new dynamic PLS model is developed to deal with the quality-related fault diagnosis issue for dynamic processes. In addition, to handle the non-Gaussian property of the dynamic variables, an independent component analysis (ICA) model is successfully combined with the dynamic PLS model. Finally, the proposed method is firstly examined using the Tennessee Eastman process, where it is shown that the new methods perform better than the existing methods. Then they are applied to a real HSMP, where the effectiveness is further convinced from real industrial data.