Abbas RASAIENIA, Behzad MOSHIRI, Mohammadamin MOEZZI

Feature-based fault detection of industrial gas turbines using neural networks

Gas turbine (GT) fault detection plays a vital role in the minimization of power plant operation costs associated with power plant overhaul time intervals. In other words, it is helpful in generating pre-alarms and paves the way for corrective actions in due time before incurring major equipment failures. Hence, finding an efficient fault detection technique that is applicable in the online operation of power plants involved with minor computations is an urgent need in the power generation industry. Such a method is studied in this paper for the V94.2 class of GTs. As the most leading stage for developing a feature-based fault detection system and moving from a fixed time-scheduled maintenance to a condition-based one, principal component analysis is used for dimension reduction in the sensor data space and dimensionless key features are employed instead. One healthy condition and 6 faulty conditions are used to provide a realistic data set that is used for feature extraction, training, and testing artificial neural networks. In the proposed method, multilayer perceptron (MLP) and learning vector quantization (LVQ) networks are used for the fault classification. The good performance of the LVQ networks is presented by properly selecting the network architecture and respective initial weight vectors. When comparing the results of the MLP and LVQ networks for the fault classification, the LVQ network shows better classification results.

Anahtar Kelimeler:

Feature extraction, fault detection, gas turbine, principle component analysis, neural network

Feature-based fault detection of industrial gas turbines using neural networks

Keywords:

Feature extraction, fault detection, gas turbine, principle component analysis, neural network,

PDF

___

R.F. Stengel, “Toward intelligent flight control”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, pp. 1699–1717, 1993.
T. Sorsa, H.N. Koivo, H. Koivisto, “Neural networks in process fault diagnosis”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 21, pp. 815–825, 1991.
Y. Maki, K.A. Loparo, “A neural network approach to fault detection and diagnosis in industrial processes”, IEEE Transactions on Control Systems Technology, Vol. 5, pp. 529–541, 1997.
M. Ayoubi, R. Isermann, “Neuro-fuzzy systems for diagnosis”, Fuzzy Sets and Systems, Vol. 89, pp. 289–307, 1997. M.M. Polycarpou, A. Vemuri, “Learning methodology for failure detection and accommodation”, IEEE Control Systems Magazine, Vol. 15, pp. 16–24, 1995.
A. Vemuri, M.M. Polycarpou, “Robust nonlinear fault diagnosis in input-output systems”, International Journal of Control, Vol. 68, pp. 343–360, 1997.
M. Demetriou, M.M. Polycarpou, “Incipient fault diagnosis of dynamical systems using online approximators”, IEEE Transactions on Automatic Control, Vol. 43, pp. 1612–1617, 1998.
K.M. Passino, P.J. Antsaklis, “Fault detection and identification in an intelligent restructurable controller”, Journal of Intelligent and Robotic Systems, Vol. 1, pp. 145–161, 1988.
E.G. Laukonen, K.M. Passino, V. Krishnaswami, G.C. Luh, G. Rizzoni, “Fault detection and isolation for an experimental internal combustion engine via fuzzy identification”, IEEE Transactions on Control Systems Technology, Vol. 3, pp. 347–355, 1995.
H. Schneider, P. Frank, “Observer based supervision and fault detection in robots using nonlinear and fuzzy logic residual evaluation”, IEEE Transactions on Control Systems Technology, Vol. 4, pp. 274–282, 1996.
P.M. Frank, B. Koppen-Seliger, “Fuzzy logic and neural network application to fault diagnosis”, International Journal of Approximate Reasoning, Vol. 16, pp. 67–88, 1997.
R. Isermann, “On fuzzy logic applications for automatic control, supervision and fault diagnosis”, IEEE Transactions on Systems, Man, and Cybernetics, Part A, Vol. 28, pp. 221–235, 1998.
W.A. Kwong, K.M. Passino, E.G. Laukonen, S. Yurkovich, “Expert supervision of fuzzy learning systems for faulttolerant aircraft control”, Proceedings of the IEEE, Special Issue on Fuzzy Logic in Engineering Applications, Vol. 83, pp. 466–483, 1995.
Y. Zhang, J. Jiang, “Bibliographical review on reconfigurable fault - tolerant control systems”, Annual Reviews in Control, Vol. 32, pp. 229–252, 2008.
M. Basseville, I. Nikiforov, “Detecting changes in signals and systems a survey”, Automatica, Vol. 24, pp. 309–326, 19 S. Haykin, Neural Networks: A Comprehensive Foundation, New York, Macmillan Publishers, 1994.
T. Kohonen, Self-Organization and Associative Memory, 2nd ed., Berlin, Springer-Verlag, 1987.
H. Demuth, M. Beale, Neural Network Toolbox (for use with MATLAB), MathWorks, Inc. User’s Guide Version 3, 2000.
K. Diamantaras, S. Kung, Principal Component Analysis Neural Networks: Theory and Applications, New York, Wiley, 1996.
A.M. Martinez, A.C. Kak, “PCA versus LDA”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, pp. 228–233, 2001.
J. Yang, Z. Jind, J. Yang, D. Zhang, A.F. Frangi, “Essence of kernel Fisher discriminant: KPCA plus LDA”, Journal of Pattern Recognition, Vol. 37, pp. 2097–2100, 2004.
Y. Li, M.J. Pont, N.N. Jones, J.A. Twiddle, “Using MLP and RBF classifiers in embedded condition monitoring and fault diagnosis applications”, Transactions of the Institute of Measurement and Control, Vol. 23, pp. 313–339, 200 S. Simani, C. Fantuzzi, P.R Spina, “Application of a neural network in gas turbine control sensor fault detection”, Proceedings of the 1998 IEEE International Conference on Control Applications, Trieste.
C. Li, J. Jeng, “Multiple sensor fault diagnosis for dynamic processes”, ISA Transactions, Vol. 49, pp. 415–432, 20 M. Zedda, R. Singh, “Neural-network-based sensor validation for gas turbine test bed analysis”, Proceedings of the IMechE, Part I: Journal of Systems and Control Engineering, Vol. 215, pp. 47–56, 2001.
S.O.T Ogaji, R. Singh, “Advanced engine diagnostic using artificial neural networks”, Applied Soft Computing, Vol. 3, pp. 259–271, 2003.
R. Verma, N. Roy, R. Ganguli, “Gas turbine diagnostics using a soft computing approach”, Applied Mathematics and Computation, Vol. 172, pp. 1342–1363, 2005.
W.Z. Yan, J.C. Li, K.F. Goebel, “On improving performance of aircraft engine gas path fault diagnosis”, Transactions of Institute of Measurement and Control, Vol. 31, pp. 275–291, 2009.
N.H. Afgan, M.G. Cavalho, P.A. Pilavachi, A. Tourlidakis, G.G. Olkhonski, N. Martins, “An expert system concept for diagnosis and monitoring of gas turbine combustion chambers”, Applied Thermal Engineering, Vol. 26, pp. 766–771, 2006.
M. Fast, M. Assadi, S. De, “Development and multi-utility of an ANN model for an industrial gas turbine”, Applied Energy, Vol. 86, pp. 9–17, 2009.
Y.G. Li, P. Nilkitsaranont, “Gas turbine performance prognostic for condition-based maintenance”, Applied Energy, Vol. 86, pp. 2152–2161, 2009.
J. Arriagada, M. Genrup, A. Loberg, M. Assadi, “Fault diagnosis system for an industrial gas turbine by means of neural networks”, Proceedings of the International Gas Turbine Congress, pp. 2–7, 2003.
Tuga Co. Publication Operation and Maintenance Manuals for V94.2 Gas Turbines, Damavand Combined Cycle Power Plant Archive, Revision D, 2008.