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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

Figures

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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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