Multisubspace Orthogonal Canonical Correlation Analysis for Quality-Related Plant-Wide Process Monitoring

Abstract

Plant-wide processes often have the characteristics of large-scale and multiple operating units. Moreover, due to the closed-loop control, it is possible that the fault never affects product quality. In this article, a novel data-driven method called multisubspace orthogonal canonical correlation analysis (CCA) is proposed, which can not only tell whether the fault occurs but can also judge whether the fault affects the product quality in real time. First, to reduce process analysis complexity and to construct an accurate monitoring model, the original process variable space is divided into four subspaces. Second, the developed orthogonal CCA is conducted on process data and quality data for correlation feature extraction. Then, the quality-related and quality-unrelated features are obtained. Afterward, a total of six monitoring statistics are constructed and integrated to four statistics with physical interpretation via the Bayesian fusion strategy. Finally, the developed method is tested under an industrial case.