Abstract and Applied Analysis

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

Shen Yin, Xin Gao, Hamid Reza Karimi, and Xiangping Zhu

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

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.

Article information

Source
Abstr. Appl. Anal. Volume 2014, Special Issue (2014), Article ID 836895, 8 pages.

Dates
First available in Project Euclid: 2 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412278130

Digital Object Identifier
doi:10.1155/2014/836895

Citation

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


Export citation

References

  • S. Yin, Data-driven design of fault diagnosis systems [Ph.D. dissertation], University of Duisburg-Essen, 2012.
  • V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
  • B. E. Boser, I. M. Guyon, and V. N. Vapnik, “Training algorithm for optimal margin classifiers,” in Proceedings of the 5h Annual ACM Workshop on Computational Learning Theory, pp. 144–152, New York, NY, USA, July 1992.
  • C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
  • A. Kulkarni, V. K. Jayaraman, and B. D. Kulkarni, “Support vector classification with parameter tuning assisted by agent-based technique,” Computers and Chemical Engineering, vol. 28, no. 3, pp. 311–318, 2004.
  • L. H. Chiang, M. E. Kotanchek, and A. K. Kordon, “Fault diagnosis based on Fisher discriminant analysis and support vector machines,” Computers and Chemical Engineering, vol. 28, no. 8, pp. 1389–1401, 2003.
  • K. R. Beebe, R. J. Pell, and M. B. Seasholtz, Chemometrics: A Practical Guide, Wiley, New York, NY, USA, 1998.
  • L. H. Chiang, E. L. Russell, and R. D. Braatz, Fault Detection and Diagnosis in Industrial Systems, Springer, New York, NY, USA, 2001.
  • A. Raich and A. Çinar, “Statistical process monitoring and disturbance diagnosis in multivariable continuous processes,” AIChE Journal, vol. 42, no. 4, pp. 995–1009, 1996.
  • L. H. Chiang, E. L. Russell, and R. D. Braatz, “Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis,” Chemometrics and Intelligent Laboratory Systems, vol. 50, no. 2, pp. 243–252, 2000.
  • D. R. Baughman, Neural Networks in Bioprocessing and Chemical Engineering, Academic Press, New York, NY, USA, 1995.
  • T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, NY, USA, 2009.
  • A. S. Naik, S. Yin, S. X. Ding, and P. Zhang, “Recursive identification algorithms to design fault detection systems,” Journal of Process Control, vol. 20, no. 8, pp. 957–965, 2010.
  • S. X. Ding, S. Yin, P. Zhang, E. L. Ding, and A. Naik, “An approach to data-driven adaptive residual generator design and implementation,” in Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pp. 941–946, Barcelona, Spain, July 2009.
  • S. Ding, P. Zhang, S. Yin, and E. Ding, “An integrated design framework of fault-tolerant wireless networked control systems for industrial automatic control applications,” IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 462–471, 2013.
  • H. Zhang, Y. Shi, and A. Mehr, “On ${H}_{\infty }$ filtering for discrete-time takagi-sugeno fuzzy systems,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 2, pp. 396–401, 2012.
  • H. Zhang and Y. Shi, “Parameter dependent ${H}_{\infty }$ filtering for linear time-varying systems,” Journal of Dynamic Systems, Measurement, and Control, vol. 135, no. 2, Article ID 0210067, 7 pages, 2012.
  • Y. W. Zhang, “Enhanced statistical analysis of nonlinear process using KPCA, KICA and SVM,” Chemical Engineering Science, vol. 64, pp. 800–801, 2009.
  • M. Misra, H. H. Yue, S. J. Qin, and C. Ling, “Multivariate process monitoring and fault diagnosis by multi-scale PCA,” Computers and Chemical Engineering, vol. 26, no. 9, pp. 1281–1293, 2002.
  • S. Ding, S. Yin, K. Peng, H. Hao, and B. Shen, “A novel scheme for key performance indicator prediction and diagnosis with application to an industrial hot strip mill,” IEEE Transactions on Industrial Informatics, vol. 9, no. 4, pp. 2239–2247, 2013.
  • B. Scholkopf and A. J. Smola, Learning with Kernels, Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, New York, NY, USA, 2002.
  • N. F. Thornhill and A. Horch, “Advances and new directions in plant-wide disturbance detection and diagnosis,” Control Engineering Practice, vol. 15, no. 10, pp. 1196–1206, 2007.
  • C. W. Hsu, C. C. Chang, and C. Lin, A Practical Guide to Support Vector Classification, Department of Computer Science, National Taiwan University, 2010.
  • T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, New York, NY, USA, 2001.
  • J. C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright, “Convergence properties of the Nelder-Mead simplex method in low dimensions,” SIAM Journal on Optimization, vol. 9, no. 1, pp. 112–147, 1998.
  • A. Kulkarni, V. K. Jayaraman, and B. D. Kulkarni, “Knowledge incorporated support vector machines to detect faults in Tennessee Eastman process,” Computers and Chemical Engineering, vol. 29, no. 10, pp. 2128–2133, 2005.
  • S. R. Gunn, Support Vector Machines for Classification and Regression, Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science, 1998.
  • A. Widodo and B. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2560–2574, 2007.
  • C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998.
  • V. N. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998.
  • N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, 2000.
  • B. S. Dayal and J. F. Macgregor, “Improved PLS algorithms,” Journal of Chemometrics, vol. 11, no. 1, pp. 73–85, 1997.
  • P. R. Lyman and C. Georgakis, “Plant-wide control of the Tennessee Eastman problem,” Computers and Chemical Engineering, vol. 19, no. 3, pp. 321–331, 1995.
  • J. J. Downs and E. F. Vogel, “A plant-wide industrial process control problem,” Computers and Chemical Engineering, vol. 17, no. 3, pp. 245–255, 1993.
  • S. Yin, S. X. Ding, A. Haghani, H. Hao, and P. Zhang, “A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark tennessee eastman process,” Journal of Process Control, vol. 22, pp. 1567–1581, 2012. \endinput