A Preliminary Study for Aircraft Engine Health Management based on Multi-Scale Kalman Filters

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jul 14, 2017
Seokgoo Kim Yuri Yoon Heeseong Kim Joo-Ho Choi

Abstract

Aircraft engine is directly associated with flight safety. Its unpredicted failures lead to catastrophic accident and downtime. To prevent these problems, prediction of the accurate remaining useful life of engine is essential. With the rapid development of sensor technology, engine health condition can be monitored with multiple sensors. Therefore, it is important to develop suitable methodologies of integrating various data to improve the accuracy of remaining useful life. This paper attempts to establish fusion methods after examining the existing model based and data driven methods used for the remaining useful life estimation of gas turbine engine. The developed methods are evaluated using the simulated data set C-MAPSS, which includes the parameters associated with degradation in the fan, compressor and turbine during a series of flights.

Abstract 50 | PDF Downloads 52

##plugins.themes.bootstrap3.article.details##

Keywords

PHM

References
Tumer, I., and Bajwa, A, (1999). A survey of aircraft engine health monitoring systems. 35th Joint Propulsion Conference and Exhibit.
Jaw, L. C, (2005), Recent advancements in aircraft engine health management (EHM) technologies and recommendations for the next step. ASME turbo expo 2005: Power for land, sea, and air. American Society of Mechanical Engineers.
Volponi, A., & Wood, B, (2014). Gas turbine engine health management: past, present, and future trends. Journal of Engineering for Gas Turbines and Power.
T. Kobayashi and D. L. Simon, (2007). Integration of on-line and off-line diagnostic algorithms for aircraft engine health management, Journal of Engineering for Gas Turbines and Power, Vol. 129, pp. 986-993.
Wang, P., and Gao, RX, (2014). Particle filtering-based system degradation prediction applied to jet engines. Annual conference of the prognostics and health management society.
Frederick, D.K., DeCastro, J.A., and Litt, J.S., (2007).User’s Guide for the Commercial Modular Aero-Propulsion System Simulation (CMAPSS), NASA/TM—2007-215026, Oct.
Saxena, A., & Goebel, K, (2008). Phm08 challenge data set, nasa ames prognostics data repository. Moffett Field, CA. Retrieved from [http://ti.arc.nasa.gov/project/prognostic-data-repository]
Zaidan, Martha A., Andrew R. Mills, and Robert F. Harrison, (2013). Bayesian framework for aerospace gas turbine engine prognostics. Aerospace Conference, 2013 IEEE.
Macmann, Owen B, (2016). Performing Diagnostics & Prognostics On Simulated Engine Failures Using Neural Networks. 52nd AIAA/SAE/ASEE Joint Propulsion Conference.
Xu, J., Wang, Y., & Xu, L, (2014). PHM-oriented integrated fusion prognostics for aircraft engines based on sensor data. IEEE Sensors Journal, Vol.14(4), pp. 1124-1132.
Section
Special Session Papers