Abstract
Fault diagnosis is a small sample problem as fault data are absent in the real production process. To tackle it, Support Vector Machines (SVM) is adopted to diagnose the chemical process steady faults in this paper. Considering the high data dimensionality in the large-scaled chemical industry seriously spoil classification capability of SVM, a modified adaptive chaotic binary ant system (ACBAS) is proposed and combined with SVM for fault feature selection to remove the irrelevant variables and ensure SVM classifying correctly. Simulation results and comparisons of Tennessee Eastman Process show the developed ACBAS can find the essential fault feature variables effectively and exactly, and the SVM fault diagnosis method combined with ACBAS-based feature selection greatly improve the diagnosing performance as unnecessary variables are eliminated properly.
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References
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)
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)
Chiang, L.H., Pell, R.J.: Genetic algorithms combined with discriminant analysis for key variable identification. Journal of Process Control 14, 143–155 (2004)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence from Natural to Artificial System. Oxford University Press, Oxford (1999)
Marco, D., Christian, B.: Ant colony optimization theory: A survey. Theoretical Computer Science 344, 243–278 (2005)
Wang, L., Smit, K.: On chaotic simulated annealing. IEEE Trans. Neural Networks 9, 716–718 (1998)
Chen, L., Aihara, K.: Global searching ability of chaotic neural networks. IEEE Trans. Circuit Syst. I 46, 974–993 (1999)
Determan, J., Foster, J.A.: Using chaos in genetic algorithm. In: Proceedings of the 1999 Congress on Evolutionary Computation, vol. 3, pp. 2094–2101. IEEE Press, Piscataway (1999)
Liu, J., Cai, Z.X., Liu, J.Q.: Premature convergence in genetic algorithm: Analysis and prevention based on chaos operator. In: Proc. 3rd World Congress Intelligence Control Automation, Hefei, China, pp. 495–499 (2000)
Riccardo, C., Fortuna, L., Stefano, F., Maria, G.X.: Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Transactions on evolutionary computation 7, 289–304 (2003)
Flavio, C., Fortuna, L., Mattia, F., Luca, P.: Chaotic sequences in ACO algorithms. In: Proceedings of 2004 IEEE International Symposium on Circuits and Systems, vol. 4, pp. 513–516. Institute of Electrical and Electronics Engineers Inc., Piscataway (2004)
Vladimir, N.V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Ge, M., Du, R.: Fault diagnosis using support vector machine with an application in sheet metal stamping operations. Mechanical Systems and Signal Processing 18, 143–159 (2004)
Samanta, B., Al-Balushi, K.R.: Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence 16, 657–665 (2003)
Samanta, B.: Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing 18, 625–644 (2004)
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)
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)
Downs, J.H., Vogel, E.F.: A plant-wide industrial process control problem. Comput. Chem. Eng. 17, 245–255 (1993)
Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, Heidelberg (2001)
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Wang, L., Yu, J. (2006). A Modified Adaptive Chaotic Binary Ant System and Its Application in Chemical Process Fault Diagnosis. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_66
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DOI: https://doi.org/10.1007/11881223_66
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