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
fault diagnosis, startup, turbine engine, LRU
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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