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.