Fault Diagnosis based on DPCA and CA

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 Correspondence Analysis (CA). The study is validated with experimental databases taken from an industrial process. The performance of methods is compared using the Receiver Operating Characteristics (ROC) graph with respect to several tuning parameters. The diagnosis 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 CAare 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.