This paper discusses the use of diagnostics based on machine learning (ML) within a flight
test context. The paper begins by discussing some of the problems associated with
instrumenting a test aircraft and how they could be ameliorated using ML-based
diagnostics. We then describe a number of types of supervised ML algorithms which can be
used in this context. In addition, key practical aspects of applying these algorithms, such as
feature engineering and parameter selection, are also discussed. The paper then outlines a
real-world application developed by Curtiss-Wright, called Machine Learning for Advanced
System Diagnostics (MLASD). This description includes key challenges that were
encountered during the development process and how suitable input features were
identified. Real-world results are also presented. Finally, we suggest some further
applications of ML techniques, in addition to describing other areas of development.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/624227 |
| Date | 11 1900 |
| Creators | Cooke, Alan, Melia, Thomas, Grayson, Siobhan |
| Contributors | Curtiss-Wright, University College Dublin, Insight Centre for Data Analytics |
| Publisher | International Foundation for Telemetering |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Proceedings |
| Rights | Copyright © held by the author; distribution rights International Foundation for Telemetering |
| Relation | http://www.telemetry.org/ |
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