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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/836895 |
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| _version_ | 1850172656084582400 |
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| author | Shen Yin Xin Gao Hamid Reza Karimi Xiangping Zhu |
| author_facet | Shen Yin Xin Gao Hamid Reza Karimi Xiangping Zhu |
| author_sort | Shen Yin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ca399c8cb2644d1398eff4fdbba28e6e |
| institution | OA Journals |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-ca399c8cb2644d1398eff4fdbba28e6e2025-08-20T02:20:01ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/836895836895Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman ProcessShen Yin0Xin Gao1Hamid Reza Karimi2Xiangping Zhu3College of Engineering, Bohai University, Liaoning 121013, ChinaCollege of Engineering, Bohai University, Liaoning 121013, ChinaDepartment of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, NorwayCollege of Engineering, Bohai University, Liaoning 121013, ChinaThis 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.http://dx.doi.org/10.1155/2014/836895 |
| spellingShingle | Shen Yin Xin Gao Hamid Reza Karimi Xiangping Zhu Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process Abstract and Applied Analysis |
| title | Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process |
| title_full | Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process |
| title_fullStr | Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process |
| title_full_unstemmed | Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process |
| title_short | Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process |
| title_sort | study on support vector machine based fault detection in tennessee eastman process |
| url | http://dx.doi.org/10.1155/2014/836895 |
| work_keys_str_mv | AT shenyin studyonsupportvectormachinebasedfaultdetectionintennesseeeastmanprocess AT xingao studyonsupportvectormachinebasedfaultdetectionintennesseeeastmanprocess AT hamidrezakarimi studyonsupportvectormachinebasedfaultdetectionintennesseeeastmanprocess AT xiangpingzhu studyonsupportvectormachinebasedfaultdetectionintennesseeeastmanprocess |