A Just-In-Time-Learning-Aided Canonical Correlation Analysis Method for Multimode Process Monitoring and Fault Detection

Abstract

In this article, a just-in-time-learning (JITL)-aided canonical correlation analysis (CCA) is proposed for the monitoring and fault detection of multimode processes. A canonical correlation analysis (CCA)-based fault detection method has been applied to single-operating-mode processes. However, CCA has limitations in handling processes with multiple operating points. These limitations are illustrated by a numerical example. To reduce the time for searching relevant data, K -means is integrated into the JITL to build the local CCA model. Furthermore, the proposed method is compared with commonly used kernel-based methods in terms of computational complexity and interpretability of the results. Finally, the validity and efficacy of the proposed method are shown using an industrial benchmark process. Results show that the proposed method has better performance than conventional methods in terms of fault detection rate while still tracking changes in the system.