| Field |
Value |
Language |
|
dc.contributor.author |
Puchalski, R |
|
|
dc.contributor.author |
Ha, Q
https://orcid.org/0000-0003-0978-1758
|
|
|
dc.contributor.author |
Giernacki, W |
|
|
dc.contributor.author |
Nguyen, HAD |
|
|
dc.contributor.author |
Nguyen, LV |
|
|
dc.date.accessioned |
2024-05-19T05:22:45Z |
|
|
dc.date.available |
2024-05-19T05:22:45Z |
|
|
dc.identifier.citation |
Journal of Intelligent & Robotic Systems, 110, (2) |
|
|
dc.identifier.issn |
1573-0409 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/179032
|
|
|
dc.description.abstract |
<jats:title>Abstract</jats:title><jats:p>Unmanned aerial vehicles are being used increasingly in a variety of applications. They are more and more often operating in close proximity to people and equipment. This necessitates ensuring maximum stability and flight safety. A fundamental step to achieving this goal is timely and effective diagnosis of possible defects. Popular data-based methods require a large amount of data collected during flights in various conditions. This paper describes an open PADRE database of such measurements for the detection and classification of the most common faults - multirotor propeller failures. It presents the procedure of data acquisition, the structure of the repository and ways to use the various types of data contained therein. The repository enables research on drone fault detection to be undertaken without time-consuming preparation of measurement data. The database is available on GitHub at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/AeroLabPUT/UAV_measurement_data">https://github.com/AeroLabPUT/UAV_measurement_data</jats:ext-link>. The article also introduces new and universal quality indicators for evaluating classifiers with non-uniform parameters, are proposed. They allow comparison of methods tested for a variety of fault classes and with different processing times.</jats:p> |
|
|
dc.language |
en |
|
|
dc.publisher |
Springer Science and Business Media LLC |
|
|
dc.relation.ispartof |
Journal of Intelligent & Robotic Systems |
|
|
dc.relation.isbasedon |
10.1007/s10846-024-02101-7 |
|
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.subject |
0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences |
|
|
dc.subject.classification |
Industrial Engineering & Automation |
|
|
dc.subject.classification |
4007 Control engineering, mechatronics and robotics |
|
|
dc.subject.classification |
4602 Artificial intelligence |
|
|
dc.title |
PADRE – A Repository for Research on Fault Detection and Isolation of Unmanned Aerial Vehicle Propellers |
|
|
dc.type |
Journal Article |
|
|
utslib.citation.volume |
110 |
|
|
utslib.for |
0801 Artificial Intelligence and Image Processing |
|
|
utslib.for |
1702 Cognitive Sciences |
|
|
pubs.organisational-group |
University of Technology Sydney |
|
|
pubs.organisational-group |
University of Technology Sydney/Faculty of Engineering and Information Technology |
|
|
pubs.organisational-group |
University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure |
|
|
pubs.organisational-group |
University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering |
|
|
utslib.copyright.status |
open_access |
* |
|
dc.date.updated |
2024-05-19T05:22:38Z |
|
|
pubs.issue |
2 |
|
|
pubs.publication-status |
Published online |
|
|
pubs.volume |
110 |
|
|
utslib.citation.issue |
2 |
|
