Abstract and Applied Analysis
- Abstr. Appl. Anal.
- Volume 2014, Special Issue (2014), Article ID 836895, 8 pages.
Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process
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.
Abstr. Appl. Anal. Volume 2014, Special Issue (2014), Article ID 836895, 8 pages.
First available in Project Euclid: 2 October 2014
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Yin, Shen; Gao, Xin; Karimi, Hamid Reza; Zhu, Xiangping. Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process. Abstr. Appl. Anal. 2014, Special Issue (2014), Article ID 836895, 8 pages. doi:10.1155/2014/836895. https://projecteuclid.org/euclid.aaa/1412278130