Application of principal component pursuit to process fault detection and diagnosis

Data-driven process monitoring has been extensively discussed in both academia and industry because of its applicability and effectiveness. One of the most applied techniques is the principal component analysis (PCA). Recently a new technique called principal component pursuit (PCP) is introduced. Compared to PCA, PCP is more robust to outliers. In this paper, the application of the PCP technique to process monitoring is thoroughly discussed from training data preprocessing to residual signal post-filtering. A new scaling preprocessing step is proposed to improve quality of data matrices in the sense of low coherence. A residual generator and a post-filter suitable for PCP generated process models are also provided. The post-filtered residual represents the fault signal, which makes the fault detection, isolation and reconstruction procedures simple and straightforward. A numerical example is provided to describe and illustrate the PCP-based process modeling and monitoring procedures.