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## Fault Detection and Identification of High Dimension System by GLOLIMOT | ||

International Journal of Industrial Electronics Control and Optimization | ||

مقاله 8، دوره 2، شماره 4، دی 2019، صفحه 331-342 اصل مقاله (704.38 K)
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نوع مقاله: Research Articles | ||

شناسه دیجیتال (DOI): 10.22111/ieco.2019.27300.1095 | ||

نویسندگان | ||

Seyed-mohamad-emad Oliaee^{1}؛ Mohamad Teshnehlab^{*} ^{1}؛ Mehdi Aliyari-shore-deli^{2}
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^{1}Control Engineering Dept., Electrical Faculty K.N.Toosi University of Technology, Tehran, Iran | ||

^{2}Mechatronics Dept., Electrical Faculty K.N.Toosi University of Technology, Tehran, Iran | ||

چکیده | ||

The Local Model Network (LMN) is one of the common structures to model systems and fault detection and identification. This structure covers the disadvantages of training in fuzzy systems and interpretations in neural networks at the same time. But the algorithms that have been introduced to create LMN, such as LOLIMOT, are very sensitive to the dimension of input space. In other words, the search space and the number of network parameters are increased exponentially by increasing the input dimension, which is called the curse of dimensionality. Therefore in this paper, the LMN structure has been developed, and a new incremental algorithm has been proposed which is based on Genetic algorithm and LOLIMOT algorithm that is called GLOLIMOT. The proposed idea reduces the search space dimension and also optimizes it. The proposed idea and the traditional structure are tested on single-shaft industrial gas turbine prototype model, which has high complexity and high dimension. The results indicate improvement in performance of the proposed structure and algorithm. | ||

کلیدواژهها | ||

LMN؛ LOLIMOT؛ Genetic Algorithm؛ GLOLIMOT؛ FDI | ||

مراجع | ||

[1] T. Fischer, B. Hartmann, and O. Nelles, "Increasing the Performance of a Training Algorithm for Local Model Networks," in World Congress of Engineering and Computer Science (WCECS). San Francisco, USA, 2012. [2] A. A. Adeniran and S. El Ferik, "Modeling and Identification of Nonlinear Systems: A Review of the Multimodel Approach--Part 1," 2016. [3] O. Nelles, S. Sinsel, and R. Isermann, "Local basis function networks for identification of a turbocharger," in Control'96, UKACC International Conference on (Conf. Publ. No. 427), 1996, pp. 7-12. [4] O. Nelles, Nonlinear system identification: from classical approaches to neural networks and fuzzy models: Springer Science & Business Media, 2013. [5] T. A. Johansen and B. A. Foss, "Identification of non-linear system structure and parameters using regime decomposition," Automatica, vol. 31, pp. 321-326, 1995. [6] O. Bänfer and O. Nelles, "Polynomial model tree (POLYMOT)—A new training algorithm for local model networks with higher degree polynomials," in 2009 IEEE International Conference on Control and Automation, 2009, pp. 1571-1576. [7] M. A. Nekoui and S. M. Sajadifar, "Nonlinear System Identification using Locally Linear Model Tree and Particle Swarm Optimization," in Industrial Technology, 2006. ICIT 2006. IEEE International Conference on, 2006, pp. 1563-1568. [8] R. Mehran, A. Fatehi, C. Lucas, and B. N. Araabi, "Particle swarm extension to LOLIMOT," in Sixth International Conference on Intelligent Systems Design and Applications, 2006, pp. 969-974. [9] J. Rezaie, B. Moshiri, A. Rafati, and B. N. Araabi, "Modified LOLIMOT algorithm for nonlinear centralized Kalman filtering fusion," in Information Fusion, 2007 10th International Conference on, 2007, pp. 1-8. [10] S. Jakubek and C. Hametner, "Identification of neurofuzzy models using GTLS parameter estimation," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, pp. 1121-1133, 2009. [11] S. Jakubek and N. Keuth, "A local neuro-fuzzy network for high-dimensional models and optimization," Engineering applications of artificial intelligence, vol. 19, pp. 705-717, 2006. [12] A. Sarabi-Jamab and B. N. Araabi, "PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling," Journal of Control Science and Engineering, vol. 2011, 2011. [13] A. S. Jamab and B. N. Araabi, "A learning algorithm for local linear neuro-fuzzy models with self-construction through merge & split," in 2006 IEEE Conference on Cybernetics and Intelligent Systems, 2006, pp. 1-6. [14] S. M. E. Oliaee, M. A. Shoorehdeli, and M. Teshnehlab, "Faults detecting of high-dimension gas turbine by stacking DNN and LLM," in Fuzzy and Intelligent Systems (CFIS), 2018 6th Iranian Joint Congress on, 2018, pp. 142-145. [15] D. Karaboga and E. Kaya, "Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey," Artificial Intelligence Review, pp. 1-31, 2018. [16] A. Doroshenko, "Piecewise-Linear Approach to Classification Based on Geometrical Transformation Model for Imbalanced Dataset," in 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), 2018, pp. 231-235. [17] H. Azimi, S. Shabanlou, I. Ebtehaj, H. Bonakdari, and S. Kardar, "Combination of computational fluid dynamics, adaptive neuro-fuzzy inference system, and genetic algorithm for predicting discharge coefficient of rectangular side orifices," Journal of Irrigation and Drainage Engineering, vol. 143, p. 04017015, 2017. [18] A. H. Hamamoto, L. F. Carvalho, L. D. H. Sampaio, T. Abrão, and M. L. Proença Jr, "Network anomaly detection system using genetic algorithm and fuzzy logic," Expert Systems with Applications, vol. 92, pp. 390-402, 2018. [19] O. F. Lutfy, S. B. M. Noor, and M. H. Marhaban, "A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems," Scientific Research and Essays, vol. 6, pp. 6475-6486, 2011. [20] L. Breiman, "Hinging hyperplanes for regression, classification, and function approximation," IEEE Transactions on Information Theory, vol. 39, pp. 999-1013, 1993. [21] S. Ernst, "Hinging hyperplane trees for approximation and identification," in Decision and Control, 1998. Proceedings of the 37th IEEE Conference on, 1998, pp. 1266-1271. [22] B. Hartmann, T. Ebert, T. Fischer, J. Belz, G. Kampmann, and O. Nelles, "LMNTOOL–Toolbox zum automatischen Trainieren lokaler Modellnetze," in Proceedings of the 22. Workshop Computational Intelligence (Hoffmann, F.; Hüllermeier, E., Hg.), S, 2014, pp. 341-355. [23] O. Nelles, "Axes-oblique partitioning strategies for local model networks," in 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, 2006, pp. 2378-2383. [24] B. Hartmann and O. Nelles, "On the smoothness in local model networks," in American Control Conference (ACC), St. Louis, USA (June 2009), 2009. [25] B. Hartmann and O. Nelles, "Structure trade-off strategy for local model networks," in Control Applications (CCA), 2012 IEEE International Conference on, 2012, pp. 451-456. [26] J. Xu, X. Huang, and S. Wang, "Adaptive hinging hyperplanes and its applications in dynamic system identification," Automatica, vol. 45, pp. 2325-2332, 2009. [27] S. Simani and C. Fantuzzi, "Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype," Mechatronics, vol. 16, pp. 341-363, 2006. [28] S. Simani, C. Fantuzzi, and R. Spina, "Application of a neural network in gas turbine control sensor fault detection," in Control Applications, 1998. Proceedings of the 1998 IEEE International Conference on, 1998, pp. 182-186. [29] V. Palade, R. J. Patton, F. J. Uppal, J. Quevedo, and S. Daley, "Fault diagnosis of an industrial gas turbine using neuro-fuzzy methods," IFAC Proceedings Volumes, vol. 35, pp. 471-476, 2002. [30] S. Simani, "Identification and fault diagnosis of a simulated model of an industrial gas turbine," IEEE Transactions on Industrial Informatics, vol. 1, pp. 202-216, 2005. [31] H. A. Nozari, M. A. Shoorehdeli, S. Simani, and H. D. Banadaki, "Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques," Neurocomputing, vol. 91, pp. 29-47, 2012. [32] O. Nelles, Nonlinear system identification: from classical approaches to neural networks and fuzzy models: Springer, 2001. [33] T. A. Johansen and R. Murray-Smith, "The operating regime approach to nonlinear modelling and control," Multiple model approaches to modelling and control, vol. 1, pp. 3-72, 1997. [34] V. Kecman and B. Pfeiffer, "Exploiting the structural equivalence of learning fuzzy systems and radial basis function neural networks," in Proceedings of the Second European Congress on Intelligent Techniques and Soft Computing EUFIT-94, Aachen, Gemany, 1994, pp. 58-66. [35] S. K. Halgamuge, Advanced methods for fusion of fuzzy systems and neural networks in intelligent data processing: VDI Verlag, 1996. [36] O. Nelles and B. Hartmann, "Structure Trade-off Strategy for Local Model Networks," in IEEE International Conference on Control Applications (CCA), Dubrovnik, Croatia, 2012, pp. 451-456. [37] S. Simani and R. J. Patton, "Fault diagnosis of an industrial gas turbine prototype using a system identification approach," Control Engineering Practice, vol. 16, pp. 769-786, 2008. [38] S. Simani, C. Fantuzzi, and R. J. Patton, "Model-Based Fault Diagnosis Techniques," in Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques, ed: Springer, 2003, pp. 19-60. | ||

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