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Study on Support Vector Machine‐Based Fault Detection in Tennessee Eastman Process

Author

Listed:
  • Shen Yin
  • Xin Gao
  • Hamid Reza Karimi
  • Xiangping Zhu

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.

Suggested Citation

  • Shen Yin & Xin Gao & Hamid Reza Karimi & Xiangping Zhu, 2014. "Study on Support Vector Machine‐Based Fault Detection in Tennessee Eastman Process," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:836895
    DOI: 10.1155/2014/836895
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    1. Vladimir N. Vapnik, 1995. "The Nature of Statistical Learning Theory," Springer Books, Springer, number 978-1-4757-2440-0, March.
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    Cited by:

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    2. Riham Ginzarly & Nazih Moubayed & Ghaleb Hoblos & Hassan Kanj & Mouhammad Alakkoumi & Alaa Mawas, 2025. "Assessing HMM and SVM for Condition-Based Monitoring and Fault Detection in HEV Electrical Machines," Energies, MDPI, vol. 18(13), pages 1-16, July.
    3. Yuanchang Zhong & Liang Zhang & Shaojing Xing & Fachuan Li & Beili Wan, 2014. "The Big Data Processing Algorithm for Water Environment Monitoring of the Three Gorges Reservoir Area," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).

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