A Comprehensive Evaluation of Statistics Pattern Analysis Based Process Monitoring

Statistics pattern analysis (SPA) is a new multivariate statistical monitoring framework proposed by the authors recently. It addresses some challenges that cannot be readily addressed by the commonly used multivariate statistical methods such as principal component analysis (PCA) in monitoring batch processes in the semiconductor industry. It was later extended to the monitoring of continuous processes using a moving window based approach. In this work, we perform a comprehensive comparison of SPA with representative linear and nonlinear multivariate process monitoring methods. The superior performance of SPA is demonstrated using the challenging Tennessee Eastman process (TEP).