[en] Module performance analysis is a well-established framework to assess changes in the
health condition of the components of the engine gas-path. The primary material of the
technique is the so-called vector of residuals, which are built as the difference
between actual measurement taken in the gas-path and values predicted by means of an
engine model. Obviously, the quality of the assessment of the engine condition depends strongly
on the accuracy of the engine model.
The present paper proposes a new approach for data-driven modelling of a fleet of engines
of a given type. Such black-box models can be designed by operators such as airlines and
third-party companies. The fleet-wide modelling process is formulated as a regression problem
that provides a dedicated model for each engine in the fleet, while recognising that all
engines are of the same type. The methodology is applied to a virtual fleet of engines generated
within the ProDiMES environment. The set of models is assessed quantitatively through the
coefficient of determination and is further used to perform anomaly detection.
Disciplines :
Aerospace & aeronautics engineering
Author, co-author :
Borguet, Sébastien ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale
Dewallef, Pierre ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Systèmes de conversion d'énergie pour un dévelop.durable
Léonard, Olivier ; Université de Liège - ULiège > Aérospatiale et Mécanique > Turbomachines & Propulsion
Language :
English
Title :
Regression-Based Modeling of a Fleet of Gas Turbine Engines for Performance Trending
Publication date :
February 2016
Journal title :
Journal of Engineering for Gas Turbines and Power
ISSN :
0742-4795
eISSN :
1528-8919
Publisher :
American Society of Mechanical Engineers, United States
Rajamani, R., Wang, J., and Jeong, K. Y., 2004, "Condition-Based Maintenance for Aircraft Engines," ASME Paper No. GT2004-54127.
Volponi, A. J., 2003, "Foundation of Gas Path Analysis (Part I and II)," Gas Turbine Condition Monitoring and Fault Diagnosis (VKI Lecture Series, Vol. 1), von Karman Institute, Brussels, Belgium.
Mattingly, J. D., 1996, Elements of Gas Turbine Propulsion, McGraw-Hill, New York.
Walsh, P. P., and Fletcher, P., 1998, Gas Turbine Performance, Blackwell Science, New York.
Visser, W., and Broomhead, M., 2000, "GSP: A Generic Object-Oriented Gas Turbine Simulation Environment," ASME Paper No. 2000-GT-0002.
May, R., Csank, J., Lavelle, T., Litt, J., and Guo, T.-H., 2010, "A High Fidelity Simulation of a Generic Commercial Aircraft Engine and Controller," AIAA Paper No. 2010-6630.
Verbist, M., Visser, W., and van Buijtenen, J. P., 2013, "Experience With Gas-Path Analysis for On-Wing Turbofan Condition Monitoring," ASME Paper No. GT2013-95739.
Aretakis, N., Roumeliotis, I., Alexiou, A., Romessis, C., and Mathioudakis, K., 2014, "Turbofan Engine Health Assessment From Flight Data," ASME Paper No. GT2014-26443.
Simon, D. L., and Rinehart, A. W., 2014 "A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data," ASME Paper No. GT2014-27172.
Dewallef, P., and Leonard, O., 2001, "On-Line Validation of Measurements on Jet Engines Using Automatic Learning Methods," 15th International Symposium on Air Breathing Engines, Bangalore, Sept. 2-7, Paper No. ISABE-2001-1031.
Chiras, N., Evans, C., and Rees, D., 2002, "Nonlinear Gas Turbine Modelling Using Feedforward Neural Networks," ASME Paper No. GT2002-30035.
Herzog, J., Hanlin, J., Wegerich, S., and Wilks, A., 2005, "High Performance Condition Monitoring of Aircraft Engines," ASME Paper No. GT2005-68485.
Loboda, I., Yepifanov, S., and Feldshteyn, Y., 2009, "Analysis of Maintenance Data of a Gas Turbine for Driving an Electric Generator," ASME Paper No. GT2009-60176.
Lacaille, J., and C^ome, E., 2011, "Visual Mining and Statistics for a Turbofan Engine Fleet," IEEE Aerospace Conference, Big Sky, MT, Mar. 5-12.
Loboda, I., and Feldshteyn, Y., 2010, "Polynomials and Neural Networks for Gas Turbine Monitoring: A Comparative Study," ASME Paper No. GT2010-23749.
Come, E., Cottrell, M., Verleysen, M., and Lacaille, J., 2011, "Aircraft Engine Fleet Monitoring Using Self-Organizing Maps and Edit Distance," 8th International Conference on Advances in Self-Organizing Maps (WSOM'11), Espoo, Finland, June 13-15, Springer-Verlag, New York, pp. 298-307.
Chu, E., Gorinevsky, D., and Boyd, S., 2010, "Detecting Aircraft Performance Anomalies From Cruise Flight Data," AIAA Paper No. 2010-3307.
Chu, E., Gorinevsky, D., and Boyd, S., 2011, "Scalable Statistical Monitoring of Fleet Data," 18th World IFAC Congress, Milan, Italy, Aug. 28-Sept. 2, pp. 13227-13232.
Simon, D. L., 2010, "Propulsion Diagnostic Method Evaluation Strategy (Pro-DiMES) User's Guide," NASA Glenn Research Center, Cleveland, OH, Technical Memorandum No. TM-2010-215840.
Uday, P., and Ganguli, R., 2010, "Jet Engine Health Signal Denoising Using Optimally Weighted Recursive Median Filter," ASME J. Eng. Gas Turbines Power, 132(4), p. 041601.
Simon, D. L., Borguet, S., L-eonard, O., and Zhang, X., 2014, "Aircraft Engine Gas Path Diagnostic Methods: Public Benchmarking Results," ASME J. Eng. Gas Turbines Power, 136(4), p. 041201.
Dewallef, P., and L-eonard, O., 2001, "Robust Measurement Validation on Jet-Engines," 4th European Conference on Turbomachinery, Florence, Mar. 20-23, pp. 899-909.
Borguet, S., and L-eonard, O., 2008, "A Sensor-Fault-Tolerant Diagnosis Tool Based on a Quadratic Programming Approach," ASME J. Eng. Gas Turbines Power, 130(2), p. 021605.
