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Transactions of the Institute of Measurement and Control
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On improving performance of aircraft engine gas path fault diagnosis

W.Z. Yan

Industrial Artificial Intelligence Lab, GE Global Research Center, Niskayuna, NY 12309, USA, yan{at}crd.ge.com

J.C. Li

Department of MANE, Rensselaer Polytechnic Institute, Troy, NY 12181, USA

K.F. Goebel

NASA Ames Research Center, Moffett Field, CA 94035, USA

Aircraft engine fault diagnosis plays a crucial role in cost-effective operations of aircraft engines. However, designing an engine fault diagnostic system with the desired performance is a challenging task, because of several characteristics associated with aircraft engines. Geared towards achieving the highest possible performance of fault diagnosis, this paper explores strategies on improving diagnosis performance. Specifically, we introduce flight regime mapping and a two-level multiple classifier system as means to improve classification performance. By designing a real-world aircraft fault diagnostic system, we demonstrate that the strategies adopted in this study are effective in improving the performance of aircraft engine fault diagnostic systems.

Key Words: aircraft engines • classification • classifier fusion • diagnostics • gas path diagnosis • multiple classifier systems • neural networks • support vector machine.

This version was published on June 1, 2009

Transactions of the Institute of Measurement and Control, Vol. 31, No. 3-4, 275-291 (2009)
DOI: 10.1177/0142331208092029
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