Common canonical variate analysis (CCVA) based modeling and monitoring for multimode processes

Abstract

Multiple operating modes are common in industrial processes due to feed stock alterations, product specifications, working environment changes and so on. Although different modes show different behaviors, some underlying process characteristics may stay invariable as mode changes, which reveal the essence information of the process. In this work, the issue of multimode process monitoring is studied with subspace separation, in which each mode is divided into the common subspace and specific subspace. A modified canonical variate analysis (CVA), termed as common CVA (CCVA), is put forward to extract the mode-common features based on joint approximate diagonalization method. The concatenation of all Hankel matrices is analyzed to find a common orthonormal set eigenvectors by minimization of joint diagonality criterion. Then, the remaining part of each mode is regarded as local specific subspace, which provides more representative information in each different mode. CVA algorithm is applied to build multiple local models based on mode-specific information. Two case studies, a numerical example and Tennessee Eastman (TE) process, are provided to validate the feasibility and effectiveness of the proposed method in monitoring abnormal operation for multimode processes.