Neural Network Monitoring Model for Industrial Gas Turbine

Authors

Keywords:

Artificial neural network, Fault diagnosis system, Fault monitoring system, Gas turbine, Graphical user interface

Abstract

Monitoring and diagnostic faults of industrial gas turbine are not an easy way by using conventional methods due to the nature and complexity of faults. Artificial neural network is considered an efficient tool to monitor and diagnose faults. In this paper, we proposed an efficient neural network model to monitor the gas turbine engine for on-line processing with a twofold advantage. First, the model is able to diagnose the fault in case of uncertainty or corrupted data. Second, it can predict the extent of the deterioration of the performance efficiency of the turbine engine through a simple graphical user interface. The experiment has been done on five faulty conditions and the proposed neural network model tested with new dataset. The results have proven that, the proposed model produced satisfactory results with10-10 mean square error that considered optimal results when compared with training data sets.

 

References

Kumar, A., Marzia, Z., Nita, G., & Vineet, S, “Renewable Energy System Design by Artificial Neural Network Simulation Approachâ€. In Proceedings of IEEE Electrical Power and Energy(EPEC '14):142-147,2014.

Olausson, P, “On the Selection of Methods and Tools for Analysis of Heat and Power Plants†, Division of Thermal Power engineering, Lund Institute of Technology, LUND, Sweden.P.O. Box 118, SE-221 00 ,2003.

Arriagada, J, “On the Analysis and Fault-Diagnosis Tools for Small-Scale Heat and Power Plants†,Thesis, Department of Heat and Power Engineering, Lund University, 2003.

Arriagada, J., Olausson, P., & Selimovic, A, “Artificial Neural Network Simulator for SOFC Performance Predictionâ€, Journal of Power Sources,112:54-60, 2002.

Shasha, W., Weimin, W., Yongqiang, S.,&Ya, Z, “Gas Turbine Condition Monitoring and Prognosis: A Reviewâ€, Advanced Engineering Forum, 2-3 :694-699,2012 .

Young, K., Dimitri, N.,Vitali, V., Ming, Y., & Ted, F, “A Fault Diagnosis Method for Industrial Gas Turbines Using Bayesian Data Analysisâ€,Journal of Engineering for Gas Turbines and Power, Transactions of The ASME, 132(4),2010.

Hamid, A., XiaoQi, C., &Raazesh, S, “Analysis of ANN-Based Modelling Approach for Industrial Systems â€, International Journal of Innovation, Management and Technology.4(1),2013.

Inseok, H., Sungwan, K., Youdan, K., &Chze, E, “A Survey of Fault Detection, Isolation, and Reconfiguration Methodsâ€, IEEE Trans. Contr. Sys. Tech, 18(3):636-653,2010.

Sobhani-Tehrani, E., &Khorasani, K, “Fault diagnosis of nonlinear systems using a hybrid approach .Springerâ€, Berlin,chapter 2 Germany. Lecture Notes in Control and Information Sciences, no. 383,2009.

Assadi, M., Mesbahi, E., Torisson, T., Lindquist, T., Arriagada, J. ,&Olausson, P, “A Novel Correction Technique for Simple Gas Turbine Parametersâ€. In Proceedings of ASME Turbo Expo, USA ,GT-0009, 2001.

Nesma, A., Walaa, H., &Ghada, T, “Performance Deterioration of Gas Turbine: Survey and Challenges Aheadâ€, International Journal of Engineering Research-Online 4(1),2016.

Mesbahi, E., Assadi, M., Torisson, T.,& Lindquist, T, “A Unique Correction Technique for Evaporative Gas Turbine (EvGT) Parametersâ€. Proceedings of the ASME Turbo Expo, USA.GT-0008,2001.

Arriagada, J., Genrup, M., Assadi, M.,&Loberg, A, “ Fault Diagnosis System for an Industrial Gas Turbine by Means of Neural Networksâ€, Proceedings of International Gas Turbine, Tokyo, Japan. TS-001:2-7, 2003.

Bourquin, J., Schmidli, H., van, H.,&Leuenberger, H, “ Advantages of Artificial Neural Networks (ANNs) as alternative modeling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage formâ€, European Journal of Pharmaceutical Sciences, 7(1):5–16, 1998.

Hamid, A., XiaoQi, C., Mohammad, B. M., &Raazesh, S, “ANN-based system identification for industrial systems.Gas Turbines Modeling Simulation and Controlâ€. Advanced Materials Research, 622-623:611-617,2013.

Fast, M., Assadi, M.,& De, S. , “Condition based maintenance of gas turbines using simulation data and artificial neural network: A demonstration of feasibilityâ€, In Proceedings of The ASME Turbo Expo, USA 2: 153–161,2008.

Fast, M., Palme´, .T, &Genrup, M. “A novel approach for gas turbines monitoring combining CUSUM technique and artificial neural networkâ€. Proceedings of the ASME Turbo Expo, USA, 1: 567–574, 2009.

Arriagada, J., Constantini, M., Olausson, P., Assadi, M., &Torisson, T. “Artificial Neural Network Model for a Biomass-Fueled Boiler†,ASME. Turbo Expo 2:681-688,2003.

Simani, S., & Ron, J, “Fault diagnosis of an industrial gas turbine prototype using a system identification approach, Control Engineering Practiceâ€,16(7):769–786,2008.

Guez, A., &Selinsky, J, “A neuromorphic controller with a human teacherâ€, IEEEInternational Conference on Neural Networks, 2:595-602,1988.

Tu, J. “Advantages and Disadvantages of Using Artificial Neural Networks versus Logistic Regression for Predicting Medical Outcomesâ€, Journal of Clinical Epidemiology, 49(11): 1225-1231.1996.

Hamid, A. “Modelling, Simulation and Control of Gas Turbines Using Artificial Neural Networksâ€. Ph.D. Thesis of Philosophy in Mechanical Engineering . University of Canterbury Christchurch, New Zealand, 2014.

Olausson, P., Haggstahl, D., Arriagada, J., Dahlquist ., Assadi, M, “ Hybrid model of an evaporative gas turbine power plant utilizing physical models and artificial neural networksâ€. Proceedings of the ASME Turbo Expo, USA.GT2003-38116,2003.

Kröse, B.,&Smagt, P, “An introduction to neural networks†.Eighth edition.The University of Amsterdam,1996.

Fast, M , “Artificial Neural Networks for Gas Turbine Monitoring â€, Ph.D. thesis. Sweden Lund,2010.

Fast, M., & Palme, T, “Application of artiï¬cial neural networks to the condition monitoring and diagnosis of a combined heat and power plantâ€, Journal of Energy,35:1114-1120,2010.

Fast, M., Assadi, M., & De, S, “Development and multi-utility of an ANN model for an industrial gas turbineâ€, Applied Energy, 86:9–17, 2009.

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Published

2017-06-30

How to Cite

Elashmawi, W. H., Kotp, N. A., & Tawel, G. E. (2017). Neural Network Monitoring Model for Industrial Gas Turbine. Asian Journal of Applied Sciences, 5(3). Retrieved from https://www.ajouronline.com/index.php/AJAS/article/view/4828

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