Process Monitoring Using a Novel Robust PCA Scheme

Outliers may cause model deviation and then affect the monitoring performance and hence it is a challenging problem for process monitoring. The robust principal component analysis (RPCA) approach, which describes outlier components with a sparse matrix and identifies these components using the sparse matrix recovery approach, is the most commonly used method to solve the model deviation problems caused by outliers. However, because the existing mathematical tools can only obtain a nonsparse matrix with small element values, RPCA performs poorly during process monitoring. In this paper, we propose a novel robust PCA scheme called moment-based RPCA (MRPCA). In the offline training stage, MRPCA adopts a novel outlier selection mechanism based on the difference between the higher-order and second-order central moments to select outlier samples; in the online monitoring stage, MRPCA adopts an outlier detection mechanism to distinguish outliers from fault data. Using the aforementioned mechanisms, MRPCA achieves high fault detection and low false alarm rates in tests of a numerical model and the Tennessee Eastman process.

Automated summary

This study proposes a novel robust PCA scheme called MRPCA, which adopts a difference selection mechanism for outlier samples in the offline training stage and an outlier detection mechanism for distinguishing outliers from fault data in the online monitoring stage. With these mechanisms, MRPCA achieves high fault detection rates and low false alarm rates in tests.