A Canonical Variate Analysis Based Process Monitoring Scheme and Benchmark Study

Abstract Principal component analysis (PCA) and Partial least square (PLS) are powerful multivariate statistical tools that have been successfully applied for process monitoring. They are efficient in dimension reduction and are suitable for processing large amount of data. Nevertheless, their application scope is restricted to static processes where the dynamics are ignored. In order to achieve improved monitoring performance for dynamic processes, in this paper, we propose an effective dynamic monitoring scheme based on the canonical variate analysis (CVA) technique. Different from the standard PCA- and PLS-based techniques which rely on mean-extraction for residual generation, the proposed CVA-based scheme takes process dynamics into account as well. The properties of all three methods are then compared in detail and finally, the improvements of the proposed method are demonstrated on the well-accepted Tennessee Eastman benchmark process.