Fault Diagnosis based on DPCA and CA
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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. © 2012 Elsevier B.V.