Skip to main content

Research Repository

Advanced Search

Using big-data and surface fitting to improve aircraft safety through the study of relationships and anomalies

Wooder, D.; Purvis, A.; McWilliam, R.P.

Using big-data and surface fitting to improve aircraft safety through the study of relationships and anomalies Thumbnail


Authors

D. Wooder

R.P. McWilliam



Abstract

The aim of this paper is to assess the utility of a Big-Data approach to fault detection for ‘systems of systems’, utilising the derivation of empirical relationships identified through surface fitting. So-called Big-Data Integrated Vehicle Health Management systems do currently exist, but tend to analyse the health of vehicle systems based on the behaviour of individual sensors and readings. This paper proposes that it is possible to consider vehicle systems with a ‘macro’ approach and identify relationships between key variables which may not be initially apparent. Used in this paper is the open source flight simulation software FlightGear which has previously been assessed for the development of fault detection systems with positive results. The relationships found can be combined into a model of expected results against which real-time data is tested. Surface fitting and the assessment of ‘goodness of fit’ is used to identify these relationships. It is proposed that this technique need not be limited to fault detection in vehicle systems but is also applicable to other vital systems which require redundancy and constant health analysis. This paper concludes that this method is a viable approach and that relationships can be successfully identified for fault detection purposes.

Citation

Wooder, D., Purvis, A., & McWilliam, R. (2017). Using big-data and surface fitting to improve aircraft safety through the study of relationships and anomalies. . https://doi.org/10.1016/j.procir.2016.10.126

Conference Name TESCONF-2016
Conference Location Cranfield
Acceptance Date Oct 18, 2016
Online Publication Date Mar 2, 2017
Publication Date Mar 2, 2017
Deposit Date Oct 26, 2016
Publicly Available Date Oct 27, 2016
Volume 59
Pages 172-177
Series Title PROC-CIRP18
Series ISSN 2212-8271
DOI https://doi.org/10.1016/j.procir.2016.10.126

Files

Published Conference Proceeding (Advance online version) (846 Kb)
PDF

Copyright Statement
Advance online version Crown Copyright © 2016 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.






You might also like



Downloadable Citations