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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/836895 |
| Summary: | 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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| ISSN: | 1085-3375 1687-0409 |