Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine

Author:

Joly R.B.,Ogaji S.O.T.,Singh R.,Probert S.D.

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference14 articles.

1. Performance-analysis-based gas-turbine diagnostics: a review;Li;Journal of Power and Energy, Part A, IMechE,2002

2. Friend R. A probabilistic, diagnostic and prognostic system for engine health and usage management. In: Aeropsace Conference Proceedings, 2000 IEEE, vol. 6, 18–25 March 2000. p. 185–92.

3. Green AJ. The development of engine-health monitoring for gas-turbine engine health and life management. AIAA 98-3544. In: 34th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibition, Cleveland, Ohio, USA, 13–15 July 1998.

4. Alcock A. (Application of artificial neural networks for fault diagnosis of military turbofan-engines. (MSc Thesis) Cranfield University, UK, 2002.

5. Theriault PG. Application of artificial neural-networks to the fault diagnosis of a military turbofan-engine (MSc thesis). Cranfield University, UK, 2002.

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