Open Access
2014 Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process
Shen Yin, Xin Gao, Hamid Reza Karimi, Xiangping Zhu
Abstr. Appl. Anal. 2014(SI23): 1-8 (2014). DOI: 10.1155/2014/836895


This paper investigates the proficiency of support vector machine (SVM) using datasets generated by Tennessee Eastman process simulation for fault detection. Due to its excellent performance in generalization, the classification performance of SVM is satisfactory. SVM algorithm combined with kernel function has the nonlinear attribute and can better handle the case where samples and attributes are massive. In addition, with forehand optimizing the parameters using the cross-validation technique, SVM can produce high accuracy in fault detection. Therefore, there is no need to deal with original data or refer to other algorithms, making the classification problem simple to handle. In order to further illustrate the efficiency, an industrial benchmark of Tennessee Eastman (TE) process is utilized with the SVM algorithm and PLS algorithm, respectively. By comparing the indices of detection performance, the SVM technique shows superior fault detection ability to the PLS algorithm.


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Shen Yin. Xin Gao. Hamid Reza Karimi. Xiangping Zhu. "Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process." Abstr. Appl. Anal. 2014 (SI23) 1 - 8, 2014.


Published: 2014
First available in Project Euclid: 2 October 2014

zbMATH: 07023168
Digital Object Identifier: 10.1155/2014/836895

Rights: Copyright © 2014 Hindawi

Vol.2014 • No. SI23 • 2014
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