Double monitoring of common and specific features for multimode process

Abstract

ABSTRACTA novel monitoring framework for multimode processes is proposed in this article. Generally, although the different modes have different process characteristics, they still share some common features, and the specific information is the unique characteristic that is not shared by all modes. On the basis of principal component analysis (PCA), a new statistical method, termed as local principal component analysis (LPCA), is developed for modeling multimode process data. LPCA is a modified version of PCA with the idea of maximizing local data variance, which aims to discover some directions that data, from all modes, share comparatively similar variance structure. In this low‐dimensional representation, the common features are captured and the residual corresponding to each mode can be treated as specific features. With the obtained LPCA model, both the common and specific features are utilized for monitoring. Moreover, a Bayesian inference strategy is further adopted to derive global indices of specific features for fault detection. Its feasibility and validity of the proposed method are illustrated through a simple multivariate linear system and the Tennessee Eastman challenge problem. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd.