A nonlinear full condition process monitoring method for hot rolling process with dynamic characteristic

As a typical complex industrial process, hot rolling process (HRP) is different from chemical process. Strip steels are produced coil by coil, that means there is a long idle period between coils. The rolling speed is very high and the producing time of each coil is usually a few minutes. Previous researches mostly focus on fault detection in loaded condition and very few attempts have been made to exploit the monitoring of idle condition. In order to monitor the whole process, not only the loaded condition, but also the idle one, a novel nonlinear full condition process monitoring model is developed in this work. First, a dissimilarity index (DI) is defined for condition identification and a support data vector description (SVDD) model is established to monitor the idle condition. Second, t-distributed stochastic neighbor embedding (t-SNE) is used to extract nonlinear principal components (NPC) for slow feature analysis (SFA) and cointegration analysis (CA). Nonlinear cointegration analysis (NCA) can reveal the long-run dynamic relations of nonstationary parts, while nonlinear slow feature analysis (NSFA) can extract the latent temporal dynamic and static variations of stationary ones. Finally, the monitoring performance of the proposed model is verified through a real HRP. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

Hot rolling process (HRP) is a complex industrial process that requires monitoring of both loaded and idle conditions. This study proposes a novel nonlinear full condition process monitoring model combining dissimilarity index and support data vector description for condition identification. Utilizing t-SNE to extract NPC for SFA and CA analysis, the proposed model's monitoring performance is verified through real-world application.