Comparative study of PCA approaches for fault detection: Application to a chemical reactor

Principal component analysis (PCA) has been successfully applied to several monitoring problems. However, classical PCA which is a fixed-model approach has some limitations: one of these is the inability to deal with parameter-varying process. Then an adaptation mechanism is recommended. This paper suggests a recursive PCA method for fault detection based on First Order Perturbation (RPCA-FOP). It also compares the effectiveness of the presented RPCA-FOP method and two other PCA techniques existing in literature such as the conventional PCA and the sliding window principal component analysis (SWPCA). The considered performances which are the average computation time, the missed detection rate and the false alarm rate are evaluated by simulation on a Continuous Stirred Tank Reactor (CSTR).