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Intelligent performance diagnostics of a gas turbine engine using user‐friendly interface neural networks

Intelligent performance diagnostics of a gas turbine engine using user‐friendly interface neural... In this study, in order to facilitate application of the NNs as well as to provide user‐friendly conditions, a performance diagnostic computer code using MATLAB ® was newly proposed. As a result, not only more precise and prompt analysis results can be obtained due to use of the toolbox in MATLAB ® on diagnosis and numerical analysis, but also the graphical user interface platform can be realized. The proposed engine diagnostics system is able to train the BPN with each fault pattern and then construct the total training network by assembling the trained BPNs. The database for network learning and test was constructed using a gas turbine performance simulation program. In order to investigate reliability on construction of the database for diagnostic results, an analysis is performed with five combination cases of 40 fault patterns. Finally, a diagnostic application example for the PT6A‐62 turboprop engine is performed using the trained network with the database, which represents the best diagnostic results among test sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aircraft Engineering and Aerospace Technology Emerald Publishing

Intelligent performance diagnostics of a gas turbine engine using user‐friendly interface neural networks

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Publisher
Emerald Publishing
Copyright
Copyright © 2004 Emerald Group Publishing Limited. All rights reserved.
ISSN
0002-2667
DOI
10.1108/00022660410545500
Publisher site
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Abstract

In this study, in order to facilitate application of the NNs as well as to provide user‐friendly conditions, a performance diagnostic computer code using MATLAB ® was newly proposed. As a result, not only more precise and prompt analysis results can be obtained due to use of the toolbox in MATLAB ® on diagnosis and numerical analysis, but also the graphical user interface platform can be realized. The proposed engine diagnostics system is able to train the BPN with each fault pattern and then construct the total training network by assembling the trained BPNs. The database for network learning and test was constructed using a gas turbine performance simulation program. In order to investigate reliability on construction of the database for diagnostic results, an analysis is performed with five combination cases of 40 fault patterns. Finally, a diagnostic application example for the PT6A‐62 turboprop engine is performed using the trained network with the database, which represents the best diagnostic results among test sets.

Journal

Aircraft Engineering and Aerospace Technology – Emerald Publishing

Published: Aug 1, 2004

Keywords: Gas technology; Diagnostic testing; Neural nets; Graphical user interfaces

References

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