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TECHNICAL PAPERS: Gas Turbines: Controls, Diagnostics, and Instrumentation

An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks

[+] Author and Article Information
P.-J. Lu

Institute of Aeronautics and Astronautics, National Cheng Kung University, Tainan, Taiwanpjlu@mail.ncku.edu.tw

M.-C. Zhang

Department of Jet Propulsion and Power, Beijing University of Aeronautics and Astronautics, Beijing, China

T.-C. Hsu

Institute of Aeronautics and Astronautics, National Cheng Kung University, Tainan, Taiwantjshyu@mail.ncku.edu.tw

J. Zhang

Department of Jet Propulsion and Power, Beijing University of Aeronautics and Astronautics, Beijing, Chinacdq-rfs@263.net

J. Eng. Gas Turbines Power 123(2), 340-346 (Jan 01, 2001) (7 pages) doi:10.1115/1.1362667 History: Received February 01, 2000; Revised January 01, 2001
Copyright © 2001 by ASME
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References

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Depold,  H. R., and Gass,  F. D., 1999, “The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics,” ASME J. Eng. Gas Turbines Power, 121, No. 4, pp. 607–612.

Figures

Grahic Jump Location
Comparison of engine scatters
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Autoassociative neural network architecture (8-9-5-9-8) used for eight-input data filtering
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Success rate vs noise-to-signal ratio for input data with/without autoassociative neural network (AANN) filtering
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Noise-filtering capability of AANN
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Comparison of data smoothing methods in trend detection
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“Wild” points data correction by AANN filter

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