A P-t-SNE and MMEMPM based quality-related process monitoring method for a variety of hot rolling processes. (August 2019) - Zhang, Chuanfang; Peng, Kaixiang; Dong, Jie
Abstract: A quality-related process monitoring method based on parametric t -distributed stochastic neighbour embedding (P- t -SNE) and modified minimum error minimax probability machine (MMEMPM) is proposed for a variety of hot rolling processes. This work pays close attention to the connections between different types of strip steel products instead of conventional multimode methods, which could be useful in process modelling and monitoring. First, a parametric version of t -SNE is developed for streaming data in the hot rolling process. Then, a new space separation method based on parametric t -SNE and quality variables is implemented to extract internal shared information and external unique information among different types for quality-related fault detection. After that, a variety identification method is proposed to identify the online data in the hot rolling process. Finally, the performance of proposed quality-related process monitoring method is examined through a real hot rolling process. The efficiency and feasibility are demonstrated. Highlights: a P- t -SNE is used to extract the internal shared information of streaming data . the P- t -SNE is used for space separation and quality-related fault detection. MEMPM is modified with one-versus-rest strategy for product variety identification. Application in hot rolling process is carried out to show the theoretical results.