Issue |
MATEC Web Conf.
Volume 304, 2019
9th EASN International Conference on “Innovation in Aviation & Space”
|
|
---|---|---|
Article Number | 03006 | |
Number of page(s) | 8 | |
Section | Propulsion & Engines | |
DOI | https://doi.org/10.1051/matecconf/201930403006 | |
Published online | 17 December 2019 |
F100-PW-229 Engine Fault Detection Based on Real Time Data
1
335 Fighting Squadron/116 Combat Wing, Araxos Air Base,
Achaia,
Greece
2
Hellenic Air Force Academy, Dekelia Air Base,
Attika,
Greece
3
Directory of Applied Research, HAF Electronics Depot,
Glyfada Attica,
Greece
4
Support Command, Elefsina Air Base,
Attika,
Greece
Gas turbine engines exhibit very high maintenance costs. Moreover, in the case of aero applications an in-flight engine incidence, shall, by all means, be avoided, a condition that drives total maintenance costs even higher. A measure in favor of balancing these costs is to monitor continuously the variation of engine performance data recorded during flight, establish methods to deduce useful information regarding the engine “health” status and, as a result, take appropriate actions to maintain a good engine operating condition. The current work presents such a method tailored on the “F100-PW-229” engine that is operated by the ellenic Air Force as the propulsion system of the “F-16 block 52M” aircraft [3]. CEDATS and MS Excel were the computational tools used for the current engine performance study. CEDATS is a software developed for the engine users. It provides basic data trend monitoring functions and engine fault warnings. It is well known that there is always space for improvement for such health monitoring tools since there are cases where engine operating faults are not captured. Within the frame of the current work, a data post – processing method on the engine performance data time series was applied using MS Excel, in order to raise early warnings of an uncaptured compressor operating fault.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.