Year: 2014
Type: article
Abstract: 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.
Source: Abstract and Applied Analysis
DOI: 10.1155/2014/836895
Authors: Shen Yin, Xin Gao, Hamid Reza Karimi, Xiangping Zhu
Institutions: Bohai University, University of Agder
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $562
OpenAlex ID: W2109965063