Samir Khan
Unsupervised anomaly detection in unmanned aerial vehicles
Khan, Samir; Liew, Chun Fui; Yairi, Takehisa; McWilliam, Richard
Authors
Chun Fui Liew
Takehisa Yairi
Richard McWilliam
Abstract
Having a real-time anomaly detection solution indicates a continuous stream of operational and labelled data that must satisfy a number of resource and latency requirements. Traditional solutions to the problem rely heavily on well-defined features and prior supervised knowledge, where most techniques refer to hand-crafted rules derived from known conditions. While successful in controlled situations, these rules assume that good data is available for them to detect anomalies; indicating that these rules will fail to generalise beyond known scenarios. To investigate these issues, current literature is examined for solutions that can be used to detect known and unknown anomalous instances whilst functioning as an out-of-the-box approach for efficient decision-making. The applicability of the isolation forest is discussed for engineering applications using the Aero-Propulsion System Simulation dataset as a benchmark where it is shown to outperform other unsupervised distance-based approaches. In addition, the authors have carried out real-time experiments on an unmanned aerial vehicle to highlight further applications of the method. Finally, some conclusions are drawn with respect to its simplicity and robustness in handling diagnostic problems.
Citation
Khan, S., Liew, C. F., Yairi, T., & McWilliam, R. (2019). Unsupervised anomaly detection in unmanned aerial vehicles. Applied Soft Computing, 83, Article 105650. https://doi.org/10.1016/j.asoc.2019.105650
Journal Article Type | Article |
---|---|
Online Publication Date | Aug 1, 2019 |
Publication Date | Oct 1, 2019 |
Deposit Date | Jul 26, 2019 |
Journal | Applied Soft Computing |
Print ISSN | 1568-4946 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 83 |
Article Number | 105650 |
DOI | https://doi.org/10.1016/j.asoc.2019.105650 |
Public URL | https://durham-repository.worktribe.com/output/1296525 |
You might also like
Modelling the Positional and Orientation Sensitivity of Proximity Sensors.
(2016)
Presentation / Conference Contribution
A Combined Fault Detection and Discrimination Strategy for Resource-Sensitive Platforms
(2016)
Presentation / Conference Contribution
High contrast patterns generated by phase-seeded CGH extensible to 3-D patterns and binary modulation
(2015)
Presentation / Conference Contribution
Strategies for self-repairing electronic systems
(2013)
Presentation / Conference Contribution
Holographic Lithography
(2014)
Presentation / Conference Contribution
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search