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Operation Modes Classification of Chemical Processes for History Data-Based Fault Diagnosis Methods  

Lee, Chang Jun (Department of Chemical and Biological Engineering, Seoul National University)
Ko, Jae Wook (Department of Chemical Engineering, Kwangwoon University)
Lee, Gibaek (Department of Chemical and Biological Engineering, Chungju National University)
Publication Information
Korean Chemical Engineering Research / v.46, no.2, 2008 , pp. 383-388 More about this Journal
Abstract
The safe and efficient operation of the chemical processes has become one of the primary concerns of chemical companies, and a variety of fault diagnosis methods have been developed to diagnose faults when abnormal situations arise. Recently, many research efforts have focused on fault diagnosis methods based on quantitative history data-based methods such as statistical models. However, when the history data-based models trained with the data obtained on an operation mode are applied to another operating condition, the models can make continuous wrong diagnosis, and have limits to be applied to real chemical processes with various operation modes. In order to classify operation modes of chemical processes, this study considers three multivariate models of Euclidean distance, FDA (Fisher's Discriminant Analysis), and PCA (principal component analysis), and integrates them with process dynamics to lead dynamic Euclidean distance, dynamic FDA, and dynamic PCA. A case study of the TE (Tennessee Eastman) process having six operation modes illustrates the conclusion that dynamic PCA model shows the best classification performance.
Keywords
Fault Diagnosis; Operation Mode Classification; Principal Component Analysis; Fisher Discriminant Analysis; Euclidean Distance; Tennessee Eastman Process;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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