Wear Prognostic on Turbofan Engines



Published Oct 14, 2013
Jérôme Lacaille Aurélie Gouby Olivier Piol


One of the most evident characteristic of wear for a turbofan engine is the exhaust gas temperature (EGT). It seems clear that this temperature increases when some carbon deposits on the turbine, when the compressor efficiency diminishes so the fuel flow should increase to produce the same amount of thrust, or even when some unbalance opens the spaces between the turbine and the casing. In any cases, an increase of the EGT should be analyzed because it is a wear symptom of the engine. It is mostly concluded by a water wash in the best case or a shop visit inspection and repair in the worst case. The engine manufacturer defines a schedule plan with its customer based on consumption of the EGT margin. This margin is the amount of available increase of the exhaust temperature before an inspection. Contractually, the engine is restored with a minimum EGT margin after each repair. Thus it is up to the manufacturer to understand how this margin is used to plan shop visits and to the company to estimate the current state of its engine. However, the EGT measurement is subject to a lot of noise and the company regularly washes their engines to increase randomly the margin and their capabilities. In this article we present a simple, automatic and embeddable algorithmic method to transform the successive EGT measurements in a delay indicator computed after each flight giving the amount of available use time. One challenge is to take care of the random wash or repair executed by the user. Finally this indicator may be transmitted automatically with the other data broadcasted by the aircraft computer (ACMS/ACARS) and it is used by the manufacturer to prepare his shop logistic.

How to Cite

Lacaille, J., Gouby, A. ., & Piol, O. . (2013). Wear Prognostic on Turbofan Engines. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2221
Abstract 179 | PDF Downloads 243



wear, Prognostic, aircraft engine

An, D., Choi, J., & Kim, N. H. (2012). A Comparison Study of Methods for Parameter Estimation in the Physics- based Prognostics. PHM. Minneapolis (MO).
Bellas, A., Bouveyron, C., Cottrell, M., & Lacaille, J. (2012). Robust Clustering of High-Dimensional Data. ESANN (pp. 25–27). Bruges (Bx).
Bellas, A., Bouveyron, C., Cottrell, M., & Lacaille, J. (2013). Model-based Clustering of High-dimensional Data Streams with Online Mixture of Probabilistic PCA. ADAC, 1–20.
Besse, P. (2003). Pratique de la modélisation statistique. Come, E., Cottrell, M., Verleysen, M., & Lacaille, J. (2010). Self Organizing Star (SOS) for health monitoring.
ESANN. Brugges (Bx).
Cottrell, M., Gaubert, P., Eloy, C., François, D., Hallaux,
G., Lacaille, J., & Verleysen, M. (2009). Fault prediction in aircraft engines using Self- Organizing Maps. WSOM. Miami (FL).
Flandrois, X., Lacaille, J., Masse, J.-R., & Ausloos, A. (2009). Expertise Transfer and Automatic Failure Classification for the Engine Start Capability System. AIAA InfoTech.
Lacaille, J. (1998). Synchronization of multivariate sensors with an autoadaptive neural method. Intelligent & Robotic Systems, 21(2), 155–165.
Lacaille, J. (2009a). An Automatic Sensor Fault Detection and Correction Algorithm. In American Institute of Aeronautics and Astronautics (AIAA) (Ed.), Aviation Technology, Integration, and Operations Conference (ATIO). Hilton Head (SC).
Lacaille, J. (2009b). Standardized failure signature for a turbofan engine. IEEE Aerospace conference (p. 11/0505). Big Sky (MT).
Lacaille, J. (2010). Standardization of Data used for Monitoring an Aircraft Engine. US patent 2010076468A1
Lacaille, J., & Come, E. (2011a). Visual Mining and Statistics for a Turbofan Engine Fleet. IEEE Aerospace Conference (p. 11/0405). Big Sky (MT)
Lacaille, J., & Come, E. (2011b). Sudden change detection in turbofan engine behavior. CM & MFPT. Cardiff, UK: British Institute of Non-Destructive Testing.
Lacaille, J., & Nya Djiki, R. (2009). Model Based Actuator Control Loop Fault. European Conference on Turbomachinery Fluid Dynamics and Thermodynamics. Gratz, Austria.
Liu, J. S. (2001). Monte carlo Strategies in Scientific Computing. Book (p. 245). Springer.
Saxena, A., Goebel, K., Field, M., & Filter, E. K. (2012). Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation. PHM. Minneapolis (MO).
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