Fault Diagnosis of Gas Turbine Engine LRUs Using the Startup Characteristics

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Kyusung Kim Onder Uluyol Girija Parthasarathy Dinkar Mylaraswamy

Abstract

This paper introduces a feature-extraction method to characterize gas turbine engine dynamics. The extracted features are used to develop a fault diagnosis and prognosis method for startup related sub-systems in gas turbine engines - the starter system, the ignition system and the fuel delivery system.

The startup of a gas turbine engine from ignition to idle speed is very critical not only for achieving a fast and efficient startup without incurring stall, but also for health monitoring of many subsystems involved. During startup, an engine goes through a number of phases during which various components become dominant. The proposed approach physically monitors the relevant phases of a startup by detecting distinct changes in engine behavior as it manifests in such critical variables as the core speed and the gas temperature. The startup process includes several known milestones, such as starter-on, light-off, peak gas temperature, and idle. As each of these is achieved, different engine components come into play and the dynamic response of the engine changes. Monitoring engine speed and exhaust gas temperature and their derivatives provides valuable insights into engine behavior.

The approach of the fault diagnosis system is as follows. The engine startup profiles of the core speed (N2) and the gas temperature are obtained and processed into a compact data set by identifying critical-to-characterization instances. The principal component analysis is applied to a number of parameters, and the fault is detected and mapped into three engine component failures which are the starter system failure, the ignition system failure, and the fuel delivery system failure.

How to Cite

Kim, K. ., Uluyol, O. ., Parthasarathy , G. ., & Mylaraswamy, D. . (2012). Fault Diagnosis of Gas Turbine Engine LRUs Using the Startup Characteristics. Annual Conference of the PHM Society, 4(1). https://doi.org/10.36001/phmconf.2012.v4i1.2129
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Keywords

fault diagnosis, startup, turbine engine, LRU

References
Surender, V., and Ganguli, R., (2005). Adaptive myriad filter for improved gas turbine condition monitoring using transient data. ASME J. Eng. Gas Turbines Power, vol. 127(2), pp. 329-339.

Uluyol, O., Kim, K., and Nwadiogbu, E., (2006). Synergistic use of soft computing technologies for the fault detection in gas turbine engines. IEEE Trans. Syst., Man Cybern., vol. 36(4), pp. 476-484.

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Kim, K., Uluyol, O., and Ball, C., (2005). Fault Diagnosis and Prognosis for Fuel Supply System in Gas Turbine Engines. Proceedings of ASME IDETC 2005. September 24-28, Long Beach, CA.

Uluyol, O., Kim, K., and Ball, C., (2005). On-board Characterization of Engine Dynamics for Health Monitoring and Control. Proceedings of ASME Turbo Expo 2005. June 6-9, Reno, NV.

Parthasarathy, G., Mylaraswamy, D., Uluyol, O., and Kim, K., (2011). Readiness Approach for Propulsion Engine LRUs. Proceedings of MFPT 2011. May 10-12, Virginia Beach, VA.

Mylaraswamy, D., Parthasarathy, G., Kim, K., and Uluyol, O., (2011). Low-cost Embedded Scouts for Engine Health Monitoring. ISABE Conference 2011. September 12-16, Gothenburg, Sweden.
Section
Technical Papers