Commenced in January 2007
Paper Count: 31105
Identification, Prediction and Detection of the Process Fault in a Cement Rotary Kiln by Locally Linear Neuro-Fuzzy Technique
Abstract:In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental treestructure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330331Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1397
 Isermann, R., Fault Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance. Springer, Berlin , 2005.
 Korbicz, J., Kos'cielny, J.M., Kowalczuk, Z., Cholewa, W. (Eds.) ,Fault Diagnosis. Models, Artificial Intelligence, Applications. Springer, Verlag, Berlin , 2004.
 Patton, R.J., Chen, J., Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer Academic Publishers, London, 1999.
 Patton, R.J., Korbicz, J. (Eds.), Advances in computational intelligence for fault diagnosis systems. International Journal of Applied Mathematics and Computer Science (special issue) 9(3), 1999.
 Isermann, R., On fuzzy logic applications for automatic control, supervision, and fault diagnosis. IEEE Transactions on Systems, Man and Cybernetics Part A 28 (2), 221-235, 1998.
 Ayoubi, M., Isermann, R., Neuro-fuzzy systems for diagnosis. Fuzzy Sets and System 89 (3), 289-307, 1997.
 Rutkowska, D., Neuro-Fuzzy Architectures and Hybrid Learning Springer, New York, Heidelberg, 2002.
 Babus╦çka, R., Fuzzy Modeling for Control. Kluwer Academic Publishers, London , 1998.
 Takagi, T., Sugeno, M., Fuzzy identification of systems and its application to modelling and control. IEEE Transaction on Systems, Man and Cybernetics 15 (1), 116-132, 1985.
 Czaban' ski, R., Neuro-fuzzy modeling based on a deterministic annealing approach. International Journal of Applied Mathematics 11, and Computer Science 15 (4), 561-576, 2005.
 Rutkowski, L., New Soft Computing Techniques for System Modelling, Pattern Classification and Image Processing. Springer, Berlin, 2004.
 Rutkowska, D., Zadeh, L. (Eds.), Neuro-fuzzy and soft computing. (special issue) International Journal of Applied Mathematics and Computer Science 10(4), 2000.
 O. Nelles, Nonlinear system identification. Berlin: Springer Verlag 2001.
 Iman Makaremi, Alireza Fatehi, Babak Nadjar Araabi, "Lipschitz in Numbers: A Medium for Delay Estimation," 17th IFAC world congress, Seoul, Korea, July 6-11 2008.
 Makaremi, I., Fatehi, A., Nadjar-Araabi, B., "Abnormal Condition Detection in a Cement Rotary kiln with System Identification Methods", Accepted to be published in Journal of Process Control, 2009.
 Y.Zhu, Multivariable system identification for process control. Elsevier science Ltd, 2001.
 I. Makaremi, "Intelligent Condition Monitoring of a Cement Rotary on Kiln", M.Sc. Thesis,KN Toosi Univ. of Tech, Feb 2007.
 O. Nelles, "Local linear model tree for on-line identification of time variant nonlinear dynamic systems," Proc. of International Conference on Artificial Neural Network, pp. 115-120, Bochum,Germany, 1996.
 O. Nelles and R. Isermann, "Basis function networks for interpolation of local linear models," Proc. of IEEE Conference on Decision and Control, pp. 470-475, Kobe, Japan, 1996.