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Faults detection in gas turbine using hybrid adaptive network based fuzzy inference systems

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main aim of the present paper is the implementation of a fault detection strategy to ensure the fault detection in a gas turbine which is presenting a complex system. This strategy is based on an adaptive hybrid neuro fuzzy inference technique which combines the advantages of both techniques of neuron networks and fuzzy logic, where, the objective is to maintain the desired performance of the studied gas turbine system in the presence of faults. On the other side, the representation of fuzzy knowledge in the learning neural networks has to be accurate to provide significant improvements for modeling of the studied system dynamic behavior. The results presented in this paper proves clearly that the proposed detection technique allows the perfect detection of the studied gas turbine malfunctions, furthermore it shows that the use of the proposed technique based on the Adaptive Neuro-Fuzzy Interference System (ANFIS) approach which uses the adaptive learning mechanism of neuron networks and fuzzy inference techniques, can be a promising technique to be applied in several industrial application for faults detection.
Czasopismo
Rocznik
Strony
3--17
Opis fizyczny
Bibliogr. 42 poz., rys., tab.
Twórcy
autor
  • Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa 17000 DZ, Algeria
autor
  • Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa 17000 DZ, Algeria
autor
  • Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa 17000 DZ, Algeria
autor
  • Aeronautical Aerospace Automotive Railway Engineering school, ESTACA Paris, France
Bibliografia
  • 1. Amozegar M, Khorasani K. An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines. Neural Networks 2016; 76: 106-121.
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  • 3. Barsali S, De Marco A, Giglioli R, Ludovici G, Possenti A. Dynamic modelling of biomass power plant using micro gas turbine. Renewable Energy 2015; 80: 806-818.
  • 4. Bartolini CM, Caresana F, Comodi G, Pelagalli L, Renzi M, Vagni S. Application of artificial neural networks to micro gas turbines. Energy Conversion and Management 2011; 52(1): 781-788.
  • 5. Djaidir B, Hafaifa A, Kouzou A. Vibration detection in gas turbine rotor using artificial neural network combined with continuous wavelet. Book Chapter in Advances in acoustics and vibration, Volume 5 of the series Applied Condition Monitoring 2016:101-113.
  • 6. Dandil B, Gokbulut M, Ata F. A PI Type Fuzzyneural Network Controller for Induction Motor Drives. Journal of Applied Sciences 2005; 5(7): 1286-1291.
  • 7. Chow EY, Willsky AS. Analytical redundancy and the design of robust failure detection systems, IEEE Transaction on Automatic Control 1984; 29: 603-614.
  • 8. Fontes CH, Pereira O. Pattern recognition in multivariate time series – A case study applied to fault detection in a gas turbine. Engineering Applications of Artificial Intelligence 2016; 49: 10-18.
  • 9. Mohammadi E, Montazeri-Gh M. Active fault tolerant control with self-enrichment capability for gas turbine engines. Aerospace Science and Technology 2016; 56: 70-89.
  • 10. Ablay G. A modeling and control approach to advanced nuclear power plants with gas turbines. Energy Conversion and Management 2013; 76:899-909.
  • 11. Asgari H, Chen XQ, Morini M, Pinelli M, Sainudiin R, Spina PR, Venturini M. NARX models for simulation of the start-up operation of a single-shaft gas turbine. Applied Thermal Engineering 2016; 93: 368-376.
  • 12. Pendar H, Salehi MM, Kharrat R, Zarezadeh S. Numerical and ANFIS modeling of the effect of fracture parameters on the performance of VAPEX process. Journal of Petroleum Science and Engineering, 2016, vol. 143, pp. 128-140.
  • 13. Shayeghi H, Sobhani B, Shahryari E, Akbarimajd A. Optimal neuro-fuzzy based islanding detection method for Distributed Generation. Neurocomputing 2016; 77: 478-488.
  • 14. Hanachi H, Liu J, Banerjee A, Chen Y. Sequential state estimation of nonlinear/non-Gaussian systems with stochastic input for turbine degradation estimation. Mechanical Systems and Signal Processing 2016; 72–73: 32-45.
  • 15. Duan Y, Sun L, Wang G, Wu F. Nonlinear modeling of regenerative cycle micro gas turbine. Energy 2015; 91: 168-175.
  • 16. Jang JSR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 1993; 23(3): 665-685.
  • 17. Jang JSR, Sun CT, Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Book edited by Prentice Hall, 1997.
  • 18. Jang JSR, Sun CT. Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Transactions on Neural Networks 1993; 4(1): 156-159.
  • 19. Jang JSR. Neuro-fuzzy modeling for dynamic system identification. Fuzzy Systems Symposium 1996: 320-325.
  • 20. Jang JSR. Input selection for ANFIS learning. Proceedings of the fifth IEEE international conference on fuzzy systems 1996; 2: 1493-1499.
  • 21. Jang JSR, Sun CT. Neuro-fuzzy modeling and control. Proceedings of the IEEE 1995; 83(3): 378-406.
  • 22. Salahshoor K, Khoshro MS, Kordestani M. Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neurofuzzy inference systems. Simulation Modelling Practice and Theory 2011; 19(5):1280-1293.
  • 23. Salahshoor K, Khoshro MS, Kordestani M. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers. Energy 2010; 35(12): 5472-5482.
  • 24. Kulikov GG, Thompson HA. Dynamic Modelling of Gas Turbines: Identification, Simulation, Condition Monitoring and Optimal Control, Book chapter, Series: Advances in Industrial Control, (Eds.) Springer 2004.
  • 25. Kulikov GG. Dynamic characteristic of gas turbine engine. In: Shevyakov AA, Martyanova TS, editors. Optimisation of multivariable control systems of aero gas turbine engines. Moscow, Book edited by Mashinostroenie 1989: 35-41. Russian.
  • 26. Kulikov GG. Methods for creation of linear mathematical models of gas turbine engines, In: Shevyakov AA, Martyanova TS, editors. Optimisation of Multivariable Control Systems of Aero Gas Turbine Engines. Moscow, Book edited by Mashinostroenie 1989: 41-81. Russian.
  • 27. Wie LY. A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Applied Soft Computing 2016, vol. 42, pp. 368-376.
  • 28. Rahmoune MB, Hafaifa A, Guemana M. Fault diagnosis in gas turbine based on neural networks: Vibrations Speed Application. Book Chapter in Advances in Acoustics and Vibration, Volume 5 of the series Applied Condition Monitoring 2016: 1-11.
  • 29. Mohamed IM, Fawzan S. LFC based adaptive PID controller using ANN and ANFIS techniques. Journal of Electrical Systems and Information Technology 2014; 1(3): 212-222.
  • 30. Nikpey H, Assadi M, Breuhaus P. Development of an optimized artificial neural network model for combined heat and power micro gas turbines. Applied Energy 2013; 108: 137-148.
  • 31. Nikpey H, Assadi M, Breuhaus P, Mørkved PT. Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas. Applied Energy 2014; 117: 30-41.
  • 32. Turney P, Halasz M. Contextual normalization applied to aircraft gas turbine engine diagnosis. Applied Intelligence 1993; 3(2): 109-129.
  • 33. Ringwood JV, Simani S. Overview of modelling and control strategies for wind turbines and wave energy devices: Comparisons and contrasts. Annual Reviews in Control 2015; 40: 27-49.
  • 34. Sadough Vanini ZN, Khorasani K, Meskin N. Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach. Information Sciences 2014; 259: 234-251.
  • 35. Saeed RA, Galybin AN, Popov V. 3D fluid-structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS. Mechanical Systems and Signal Processing 2013; 34(1–2): 259-276.
  • 36. Samet EA, Keith EH. Nonlinear dynamic modeling and simulation of a passively cooled small modular reactor. Progress in Nuclear Energy 2016; 91: 116-131.
  • 37. Sasan B, Saeedeh SS. Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm. International Journal of Electrical Power & Energy Systems 2016; 82: 92-104.
  • 38. Seixas M, Melício R, Mendes VMF. Simulation by discrete mass modeling of offshore wind turbine system with DC link. International Journal of Marine Energy 2016; 14: 80-100.
  • 39. Tayarani-Bathaie SS, Khorasani K. Fault detection and isolation of gas turbine engines using a bank of neural networks. Journal of Process Control 2015; 36: 22-41.
  • 40. Ghabraei S, Moradi H, Vossoughi G. Multivariable robust adaptive sliding mode control of an industrial boiler-turbine in the presence of modeling imprecisions and external disturbances: A comparison with type-I servo controller. ISA Transactions 2015; 58: 398-408.
  • 41. Yang L, Entchev E. Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique. Applied Energy 2014; 134: 197-203.
  • 42. Oğuz Y, Üstün SV, Yabanova I, Yumurtaci M, Güney İ. Adaptive neuro-fuzzy inference system to improve the power quality of a split shaft microturbine power generation system. Journal of Power Sources 2012: 196-209.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a197012d-6552-4b1a-a1d9-2cc067c83b27
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