Statistical process monitoring using multiple PCA models

Principal Component Analysis (PCA) has been successfully used to build a multivariate monitoring model for the process usually with one operation stage. However, for processes with more than one operation stages, building a single PCA model to monitoring the whole process operation performance may not be efficient and will lead to high rate of missing alarm. To treat this situation, a monitoring strategy using multiple PCA models is presented in this article based on the soft-partition algorithms. And the framework of utilizing multiple PCA model to monitor continuous process is also introduced. The application to three-tank plant demonstrates the effectiveness of the method.