I.A. Karimi and Rajagopalan Srinivasan (Editors), Proceedings of the 11th International Symposium on Process Systems Engineering, 15-19 July 2012, Singapore c ⃝ 2012 Elsevier B.V. All rights reserved. Fault Diagnosis based on DPCA and CA Celina Rea, Ruben Morales-Menendez * , Juan C. Tudón Martínez, Ricardo A. Ramírez Mendoza, Luis E. Garza Castañon Tecnológico de Monterrey, Campus Monterrey, Av. Eugenio Garza Sada 2501, Col. Tec- nológico, 64,849 Monterrey NL, México Abstract A comparison of two fault detection methods based in process history data is presented. The selected methods are Dynamic Principal Component Analysis (DPCA) and Corre- spondence Analysis (CA). The study is validated with experimental databases taken from an industrial process. The performance of methods is compared using the Receiver Op- erating Characteristics (ROC) graph with respect to several tuning parameters. The diag- nosis step for both methods was implemented through Contribution Plots. The effects of each parameter are discussed and some guidelines for using these methods are proposed. 1. Motivation Industrial process have grown in integration and complexity. Monitoring only by humans is risky and sometimes impossible. Faults are always present, early Fault Detection and Isolation (FDI ) systems can help operators to avoid abnormal event progression. DPCA and CA are two techniques based on statistical models coming from experimental data that can be used for fault diagnosis, Detroja et al. (2006b). These approaches are well known in some domains; but, there are several questions in the fault diagnosis. A ROC graph is a technique for visualizing, organizing and selecting classifiers based on their performance. ROC graph has been extended for use in diagnostic systems. In the published research works, the number of data for model’s learning the model, sampling rate, number of principal components/axes, thresholds have not been studied under same experimental databases. 2. Fundamentals A brief comparative review of both DPCA and CA approaches is presented focus in mod- elling, detection and diagnosis. Modeling for both DPCA and CA. Both methods need a statistical model of the pro- cess under normal operating conditions. The data set need be scaled to zero mean and unit variance. CA requires n t observations for p variables having a form of X (t )= [X 1 (t ) ... X p (t )] (n t ×p) ; while DPCA additionally includes some past observations (i.e. w- time delay) X (t )=[X 1 (t ) ... X 1 (t - w) ... X p (t ) ... X p (t - w)] (n t ×( p ·[w+1])) . Based on X (t ) matrix, two subspaces are built. The Principal Subspace captures major faults in the process, and the Residual Subspace considers minor faults and correlation rupture. A SCREE test, determines the number of principal and residual components (axes). By plotting the eigenvalues (singular values) of X (t ) for DPCA (CA), the principal compo- nents (axes) are the first k components (axes) before the inflection point is located, while * rmm@itesm.mx