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Proceedings Paper

Fault diagnosis in turbine engines using unsupervised neural networks technique
Author(s): Kyusung Kim; Charles Ball; Emmanuel Nwadiogbu
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Paper Abstract

A fault diagnosis system based on the neural networks clustering technique is developed for a mid-sized jet propulsion engine. The currently recorded data set for this engine has several limitations in its quality, which results in the lack of information required for the incipient fault detection and wide coverage of failure modes. Using the residuals of core speed, exhausted gas temperature and fuel flow, the developed system is designed to diagnose the failures related to combustor liner, bleed band, and exhausted gas temperature (EGT) sensor rake. The fault diagnosis system reports not only the machine condition but also the belief factor convincing the diagnostic decisions. In this work the actual flight data collected in the field is used to develop and validate the system, and the results are shown for the test on five engines which had experienced three different failures. The presented system is implemented in the form of web-based service and has demonstrated its robustness by isolating the failures successfully in the field.

Paper Details

Date Published: 12 April 2004
PDF: 9 pages
Proc. SPIE 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II, (12 April 2004); doi: 10.1117/12.542813
Show Author Affiliations
Kyusung Kim, Honeywell International Inc. (United States)
Charles Ball, Honeywell International Inc. (United States)
Emmanuel Nwadiogbu, Honeywell International Inc. (United States)


Published in SPIE Proceedings Vol. 5439:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II
Harold H. Szu; Mladen V. Wickerhauser; Barak A. Pearlmutter; Wim Sweldens, Editor(s)

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