A Modified Discrete Binary Ant Colony Optimization and Its Application in Chemical Process Fault Diagnosis

  • Ling Wang
  • Jinshou Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


Considering fault diagnosis is a small sample problem in real chemical process industry, Support Vector Machines (SVM) is adopted as classifier to discriminate chemical process steady faults. To improve fault diagnosis performance, it is essential to reduce the dimensionality of collected data. This paper presents a modified discrete binary ant colony optimization (MDBACO) to optimize discrete combinational problems, and then further combines it with SVM to accomplishing fault feature selection. The tests of optimizing benchmark functions show the developed MDBACO is valid and effective. The fault diagnosis results and comparisons of simulations based on Tennessee Eastman Process (TEP) prove the feature selection method based on MDBACO and SVM can find the essential fault variables quickly and exactly, and greatly increases the fault diagnosis correct rates as irrelevant variables are eliminated properly.


Support Vector Machine Feature Selection Fault Diagnosis Feature Selection Method Benchmark Function 


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  1. 1.
    Wang, S.W., Xiao, F.: Detection and diagnosis of AHU sensor faults using principal component analysis method. Energy Conversion and Management 45, 2667–2686 (2004)CrossRefGoogle Scholar
  2. 2.
    Lee, J.M., Yoo, C.K., Lee, I.B.: Fault detection of batch processes using multiway kernel principal component analysis. Computers and Chemical Engineering 28, 1837–1847 (2004)CrossRefGoogle Scholar
  3. 3.
    Huang, Y., Gertler, J., McAvoy, T.: Sensor and actuator fault isolation by structured partial PCA with nonlinear extensions. Journal of Process Control 10, 459–469 (2000)CrossRefGoogle Scholar
  4. 4.
    Chun, K.Z., Hong, H.: Feature selection using the hybrid of ant colony optimization and mutual information for the forecaster. In: Proceedings of Fourth International Conference on Machine Learning and Cybernetics, pp. 1728–1732 (2005)Google Scholar
  5. 5.
    Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowledge and Data Engineering 17, 491–502 (2005)CrossRefGoogle Scholar
  6. 6.
    Kwak, N., Choi, C.H.: Input feature selection for classification problems. IEEE Trans. Neural Networks 13, 143–159 (2002)CrossRefGoogle Scholar
  7. 7.
    Chiang, L.H., Pell, R.J.: Genetic algorithms combined with discriminant analysis for key variable identification. Journal of Process Control 14, 143–155 (2004)CrossRefGoogle Scholar
  8. 8.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)MATHGoogle Scholar
  9. 9.
    Fan, R.E., Chen, P.H., Lin, C.J.: Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889–1918 (2005)MathSciNetGoogle Scholar
  10. 10.
    Chiang, L.H., Kotanchek, M.E., Kordon, A.K.: Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computers and Chemical Engineering 28, 1389–1401 (2004)Google Scholar
  11. 11.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence from Natural to Artificial System. Oxford University Press, Oxford (1999)Google Scholar
  12. 12.
    Dorigo, M., Blum, C.: Ant colony optimization theory: A survey 344, 243-278 (2005)Google Scholar
  13. 13.
    Blum, C.: Ant colony optimization: introduction and recent trends. Physics of Life Reviews 2, 353–373 (2005)CrossRefGoogle Scholar
  14. 14.
    Gao, H.H., Yang, H.H., Wang, X.Y.: Ant colony optimization based network intrusion feature selection and deltection. In: Proceedings of Fourth International Conference on Machine Learning and Cybernetics, pp. 3871–3875 (2005)Google Scholar
  15. 15.
    Downs, J.H., Vogel, E.F.: A plant-wide industrial process control problem. Comput. Chem. Eng. 17, 245–255 (1993)CrossRefGoogle Scholar
  16. 16.
    Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, Heidelberg (2001)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ling Wang
    • 1
  • Jinshou Yu
    • 1
  1. 1.Research Institution of AutomationEast China University of Science & TechnologyShanghaiChina

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