Method and System for Predicting Hydraulic Valve Degradation on a Gas Turbine
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Abstract
This paper examines the development of a data-driven anomaly detection methodology for servo-actuated hydraulic valves installed in a gas turbine fuel delivery system. Degraded operation of these valves is a leading cause of unavailability for gas turbine driven power plants. Nearly
eighty potential features were generated from the limited raw sensors and control system signals through a combination of domain expertise, statistical feature extraction, and insight gains from prior physics-based simulations. Important features were down-selected by examining the behavior of the features using several years of operating data in conjunction with known field failures. Univariate statistical techniques were used to eliminate candidate features with limited capability to distinguish healthy from abnormal operation. A final machine learning model was generated using a process of recursive feature elimination. This paper will also touch on the practical implications of deploying a machine learning model in a real-time production environment.
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Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
Carter, T. J. (2005). Common failures in gas turbine blades. Engineering Failure Analysis, 12(2), 237-247.
Coenen, F., Swinnen, G., Vanhoof, K., & Wets, G. (2000). The improvement of response modeling: combining rule-induction and case-based reasoning. Expert Systems with Applications, 18(4), 307-313.
Davis, L. B. (1996). Dry low nox combustion systems for ge heavy-duty gas turbines. ASME 1996 International Gas Turbine and Aeroengine Congress and Exhibition, 3.
Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging Artificial Intelligence Applications in Computer Engineering.
Macaluso, A. (2016). Prognostic and health management system for hydraulic servoactuators for helicopters main and tail rotor. Third European Conference of the Prognostics and Health Management Society, 7(76).
Manning, C. D., Raghavan, P., & Sch¨utze, H. (2008). Introduction to information retrieval. Cambridge University Press.
Mornacchi, A., Vachtsevanos, G., & Jacazio, G. (2015). Prognostics and health management of an electrohydraulic servo actuator. Annual Conference of the Prognostics and Health Management Society.
Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
Ravi, Y., Pandey, A., & Jammu, V. (2010). Prediction of gas turbine trip due to electro hydraulic control valve system failures. ASME Turbo Expo 2010: Power for Land, Sea, and Air, 3(2), 299-306.
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