Robust Process monitoring via Stable Principal Component Pursuit

Abstract

For enhancing product quality and operation safety, statistical process monitoring has become an important technique in process industries, where principal component analysis (PCA) is a commonly used method. However, PCA assumes that the training data matrix only contains an underlying low-rank structure corrupted by dense noise. When gross sparse errors, i.e. outliers, exist, PCA often fails. In this paper, a robust matrix recovery method called stable principal component pursuit (SPCP) is utilized to solve this problem. A process modeling and monitoring procedure is developed based on SPCP, the effectiveness of which is illustrated using the benchmark Tennessee Eastman process.