Applications of Multiv Ariate Analysis Techniques for Fault Detection, Diagnosis and Isolation

Abstract

In this study, powerful multivariate tools such as Principal Component Analysis (PCA), Partial Least Squares (PLS) and Correspondence Analysis (CA) are applied to the problem of fault detection, diagnosis and identification and their efficacies are compared. Specifically, CA which has been recently adapted and studied for FDD applications is tested for its robustness when compared to other conventional and familiar methods like PCA and PLS on simulated datasets from three industry-based, high-fidelity simulation models. This study demonstrates that CA can negotiate time varying dynamics in process systems as compared to the other methods. This ability to handle dynamics is also responsible for providing robustness to CA based FDD scheme. The results also confirm previous claims that CA is a good tool for early detection and concrete diagnosis of process faults. In, the second portion of this work, a new integrated CA and Weighted Pairwise Scatter Linear Discriminant Analysis method is proposed for fault isolation and identification. This tool tries to exploit the discriminative ability of CA to clearly distinguish between faults in the discriminant space and also predict if an abnormal event presently occurring in a plant is related to any previous faults that were recorded. The proposed method was found to give positive results when applied to simulated data containing faults that are either a combination of previously recorded failures or at intensities which are different from those previously recorded.