A Data-Driven Approach for on-line Gas Turbine Combustion Monitoring using Classification Models
Given the critical nature of Gas Turbines in most industrial plants, it is a high priority to find ways of reducing maintenance costs and increasing the availability. Quickly detecting and identifying combustion anomalies enables the choice of an appropriate recovery strategy, potentially mitigating the consequences of unscheduled down time and increased maintenance costs. Monitoring the Exhaust Gas Temperature (EGT) profiles is a good means of detecting combustion problems: plugged nozzles and/or combustor and transition piece failures will always result in distorted exhaust gas temperature patterns. However the conventional monitoring systems do not allow robust discrimination between instrumental failures and real gas turbine issues; furthermore weak diagnostic methods can be source of numerous false alarms.
In this paper, we investigate the problem of monitoring the combustion chambers of a gas turbine and we attempt to address this issue by introducing a strategy for automatic and efficient patterns recognition by using Machine Learning Classification algorithms. Some historical events have been firstly retrieved and analyzed to discover which features are useful for classification. Based on the observations, two multiclass classification algorithms, one based on logistic regression, the other on Artificial Neural Networks (ANN), have been developed. Finally, real-world datasets have been used to benchmark the performance of the proposed algorithms against a traditional physics-based approach.
How to Cite
classification, data driven methods, Automatic diagnostics, Data-driven detection methodologies, Exhaust Gas Temperature, gas turbine, Artificial Neural Networks (ANN), oil and gas, combustion
James, G., Witten, D., Hastie, T., & Tibshirani, R., (2013). An Introduction to Statistical Learning. NY, USA: Springer Science + Business Media.
Hastie, T., Tibshirani, R., & Friedman, J., (2001). The Elements of Statistical Learning. NY, USA: Springer Science + Business Media.
Jlang, X., & Foster, C., (2013). Remote thermal performance monitoring and diagnostics – Turning data into knowledge. Proceedings of the ASME 2013 Power Conference, July 29 – August 1, Boston, Massachusetts, USA.
Hannes, L., Deneve, M., Vanderhaegen, E., Museur, T., (2009). Combustion dynamics data mining techniques: a way to gain enhanced insight in the combustion process of fielded gas turbines. Proceedings of ASME Turbo Expo 2009: Power for Land, Sea and Air, June 8-12, 2009, Orlando, Florida, USA.
Namburu, S. M., Azam, M. S., Luo, J., Choi, K., & Pattipati, K. R. (2007). Data-driven modeling, fault diagnosis, and optimal sensor selection for HVAC chillers. IEEE transactions on automation science and engineering, vol. 4, no. 3. doi: 10.1109/TASE.2006.888053.
Yan, W., Yu, L., Sherbahn, J., and Brahmakshatriya, U., (2013). On Optimizing Anomaly Detection Rules for Gas Turbine Health Monitoring. Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013, October 14-17, New Orleans, LA.
Haykin, S., (1999), Neural Networks - A Comprehensive Foundation, 2nd Ed., NJ, USA: Prentice Hall International.
Breeze, P., (2005), Power Generation Technologies, Oxford: Elsevier.
Labatut, V., Cherifi, H., (2011). Accuracy Measures for the Comparison of Classifiers. The 5th International Conference on Information Technology, arXiv : 1207.3790
Beale, M., Hagan, M., Demuth, H., (2011). Neural Network Toolbox™ User’s Guide. Natick, MA, USA: The MathWorks, Inc.
Ghoreyshi, M., Singh, R., (2004). Using neural network for diagnostics of an industrial gas turbine. Proceedings of ASME Turbo Expo 2004: Power for Land, Sea and Air, June 14-17, 2004, Vienna, Austria.
Landgrebe, T.C.W., Paclik, P., Duin, R.P.W., Bradley, A.P., (2006). Precision-recall operating characteristic (P-ROC) curves in imprecise environments (123-127), Hong Kong. doi: 10.1109/ICPR.2006.941.
Dreiseitl S., Ohno-Machado, L. (2002). Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical Informatics, Oct-Dec, 2000; 35(5-6):352-9.
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