Predictive Modeling of High-Bypass Turbofan Engine Deterioration



Published Oct 14, 2013
Christina Brasco Neil Eklund Mohak Shah Daniel Marthaler


The deterioration of high-bypass turbofan aircraft engines is an area of study that has the potential to provide valuable information to both engine manufacturers and users. Differences in deterioration between engines corresponding to different airlines, climates or flight patterns offer insight into ideal maintenance patterns and fine-tuned estimates on engine lifetime for airlines that operate over a wide range of conditions. In this paper, a model of high-bypass turbofan aircraft engine deterioration – based on cycle frequency, air quality, relative passenger mass and climate – and its possible application as a predictor of engine health and lifetime is described. Because the quantity of interest was long-term changes in engine health, the data set was mid- flight snapshot data, grouped as a set of time-series corresponding to different engines. Ultimately, a simple model was derived which can be used to predict how long a high-bypass turbofan engine will last under given conditions. Since all of the engines used in this study were the same configuration and model, the numeric results will be most valid when predicting health of engines of that variety. However, the approach outlined here could be used for any type of engine with enough available data. The results will allow manufacturers to provide better maintenance recommendations to owners of the assets.

How to Cite

Brasco, C., Eklund, N. ., Shah, M. ., & Marthaler, D. . (2013). Predictive Modeling of High-Bypass Turbofan Engine Deterioration. Annual Conference of the PHM Society, 5(1).
Abstract 402 | PDF Downloads 183



gas turbines, applications: aviation, enterprise health management, Engine Health Monitoring

Ackert, S. (2011). Engine Maintenance Concepts for Financiers: Elements of Turbofan Shop Maintenance Costs. Aircraft Monitor. 320/engine_mx_concepts_for_financiers___v2.pdf

Changzheng, L., Yong, L. (2006). Fault Diagnosis for an Airgraft Engine Based on Information Fusion. IEEE 3rd International Conference on Mechatronics (199-202)

Jain, A.K., Mao, J., Mohiuddin, K.M. (1996) Artificial Neural Networks: A Tutorial. IEEE Computer 29, 31- 44. %20bp.pdf

Kottek, M., Grieser, J., Beck, C., Rudolf, B., Rubel, F. (2006). World Map of the Köppen-Geiger Climate Classifiation Updated. Meteorologische Zeitschrift. 15 (6), 259-263.

Krok, M.J., Ashby, M.J. (2002). Condition-based, Diagnostic Gas Path Reasoning for Gas Turbine Engines. 2002 IEEE lntemational Conference on Control Applications. September 18-20. Glasgow, Scotland,U.K.

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability, 281-297. Berkeley, CA, USA

Ostertagova, E.,Ostertag, O. (2012). Forecasting using simple exponential smoothing method. Acta Electrotechnica et Informatica 12 (3), 62-66. DOI: 10.2478/v10198-012-0034-2

Saxena, A., Goebel, K., Simon, D., Eklund, N., (2008). Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation. International Conference on Prognostics and Health Management, 2008. pp. 1–9. IEEE.

Sugar, C.A., James, G.M. (2003). Finding the number of clusters in a data set: An information theoretic approach. Journal of the American Statistical Association. 98 (463). pp. 750-763. DOI: 10.1198/016214503000000666

Weizhong, Y., Feng, X. (2008). Jet Engine Gas Path Fault Diagnosis Using Dynamic Fusion of Multiple Classifiers. 2008 International Joint Conference on Neural Networks (1585-1591).
Poster Presentations