Performance Based Anomaly Detection Analysis of a Gas Turbine Engine by Artificial Neural Network Approach
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This present work follows our earlier research efforts on fault diagnosis and prognosis solutions considering statistical and physics based approaches. In-service performance analysis and detection of any malfunctioning in an operating small sized gas turbine engine using artificial neural network approach is the central theme of this work. The measured engine operating and performance parameters are used to train two neural network models, namely back propagation and generalized regression. Following the training and validation of the neural network model, simulation results for test data corresponding to various engine usage stages are found to be close by two models. The analysis identifies an anamoly in the simulated and measured data collected 17 months after the engine overhauling which may be attributed to deliberate adjustments in the operating parameters. A threshold for anomaly detection in terms of the probability levels for variation of the rated power capacity of the engine is also studied.
How to Cite
##plugins.themes.bootstrap3.article.details##
anomaly detection, artificial neural networks, gas turbine, overhauling, performance analysis
Hoeft, R., Janawitz, J. & Keck, R., (2003). Heavy duty gas turbine operating and maintenance considerations, GE Power Systems, GER-3620J
Sobanska, P. & Szczepaniak, P., (2006). Neural modeling of steam turbines, Proceedings of the International Multiconference on Computer Science and Information Technology, (pp. 197–205), November 6-10, 2006, Wisla, Poland
Fast, M., (2010). Artificial neural networks for gas turbine monitoring, Doctoral thesis, Faculty of Engineering, Lund University, Sweden
Fast, M. & Palmé, T., (2010). Application of artificial neural network to the condition monitoring and diagnosis of a combined heat and power plant, Journal of Energy, vol. 35 (2), pp. 1114-1120
Angeli, C. & Chatzinikolaou, A., (2004). On-Line fault detection techniques for technical systems: A survey, International Journal of Computer Science & Applications, Technomathematics Research Foundation, vol. I (1), pp. 12 – 30
Russell, S. & Norvig, P., (1995). Artificial intelligence: A modern approach, New York, Prentice-Hall Inc.
Saxena, B., Kumar, A., Srivastava, A. & Goel, A., (2011). Real time diagnostic prognostic solution for life cycle management of thermomechanical system, IEEE Canadian Conference on Electrical and Computer Engineering (pp. 999-1003), May 8-10, Niagara Falls, Canada
Kumar, A., Saxena, B., Srivastava,A. & Goel, A., (2011). Physics based prognostic health management for thermal Barrier Coating System, Proceeding.
Prognostic and Health Management Society Conference, vol. 2 (6), September 25-29, Montreal, Canada
Michael J. A. Berry & Gordon Linoff, (1997). Data Mining Techniques: For Marketing, Sales, and Customer Support, Database management, New Y ork, Computer Publishing Wiley
Boger, Z. & Guterman, H., (1997). Knowledge extraction from artificial neural network models, Proceedings of the IEEE Conference on Systems, Man and Cybernetics (pp. 3030 - 3035), October 12-15, Orlando, USA
Zhu, J., (2009). Marine diesel engine condition monitoring by use of BP neural network, Proceedings of the International Multi Conference of Engineers and Computer Scientists, Vol. II, March 18-20, Hong Kong
Kumara, K.P ., Rao, K.V .N.S. , Krishnan, K.R.& Thejaa, B., (2012). Neural network based vibration analysis with novelty in data detection for a large gas turbine, Shock and Vibration, vol. 19, pp. 25–35
Patel, P. M. & Prajapati, J.M., (2011). A review on artificial intelligent system for bearing condition monitoring, International journal of engineering science and technology, vol. 3 (2), pp. 1520-1525
Samhouri, M. , Al-Ghandoor, A., Alhaj Ali, S., Hinti, I. & Massad, I., (2009). An intelligent machine condition monitoring system using time-based analysis: neuro- fuzzy versus neural network, Jordan Journal of Mechanical and Industrial Engineering, vol. 3 (4), pp. 294 – 305.
Riad, A.M, Elminir K.H., & Elattar, M. H., (2010). Evaluation of neural networks in the subject of prognostics as compared to linear regression model, International Journal of Engineering & Technology IJET-IJENS, vol: 10 (6), pp. 52 - 58
Specht, D. F. (1991). A general regression neural network, IEEE Transactions on Neural Networks, vol. 2 (6), pp. 568-577.
Kaminski, M., (2010). General regression neural networks as rotor fault detectors of the induction motor, IEEE International Conference on Industrial Technology (ICIT'10) (pp. 1239 – 1244), March 14-17, Vina del Mar, Chile
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.