A quality-based nonlinear fault diagnosis framework focusing on industrial multimode batch processes

This paper proposes a framework for quality-based fault detection and diagnosis for nonlinear batch processes with multimode operating environment. The framework seeks to address 1) the mode partition problem using a kernel fuzzy C-clustering method, and the optimal cluster number will be guaranteed by a between-within proportion index; 2) the diagnosis problem using a contribution rate method based on an improved kernel partial least squares (PLS) model, by which better detection and diagnosis performances are provided; and 3) the classification of online measurements using a hybrid kernel PLS regression and the Bayes inference theory, where the new coming measurement can be correctly assigned to its constituent mode. The whole framework is developed for batch processes, and applied to the hot strip mill rolling process. It is shown using the real industrial data that for faults affecting the thickness and flatness of the strip steel in this process, the detection and diagnosis abilities of the present methods are better compared with the existing methods