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    The Application of Machine Learning Techniques in Flight Test Applications

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    Conference Proceedings
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    Author
    Cooke, Alan
    Melia, Thomas
    Grayson, Siobhan
    Affiliation
    Curtiss-Wright
    University College Dublin, Insight Centre for Data Analytics
    Issue Date
    2016-11
    Keywords
    FTI
    Machine Learning
    Time Series Classification
    Anomaly Detection
    Resource Constrained Environments
    
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    Show full item record
    Rights
    Copyright © held by the author; distribution rights International Foundation for Telemetering
    Collection Information
    Proceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.
    Publisher
    International Foundation for Telemetering
    Journal
    International Telemetering Conference Proceedings
    Abstract
    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.
    Sponsors
    International Foundation for Telemetering
    ISSN
    0884-5123
    0074-9079
    Additional Links
    http://www.telemetry.org/
    Collections
    International Telemetering Conference Proceedings, Volume 52 (2016)

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