DronePropA: Motion trajectories dataset for defective dronesMendeley Data
Unmanned aerial vehicles, or drones, are increasingly deployed in critical applications that demand exceptional safety and reliability. However, as drones become integral to industries such as logistics, agriculture, and public safety, reliability issues with core components like propellers can lead...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235234092500321X |
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| Summary: | Unmanned aerial vehicles, or drones, are increasingly deployed in critical applications that demand exceptional safety and reliability. However, as drones become integral to industries such as logistics, agriculture, and public safety, reliability issues with core components like propellers can lead to serious safety risks and financial losses. Propellers typically have high failure rates, especially in harsh conditions, which has encouraged research into effective health monitoring techniques for early fault detection. Despite these efforts, existing datasets on faulty propellers remain limited in scale, diversity, and coverage of fault types and severity levels. This data article introduces a comprehensive dataset composed of motion trajectories and flight logs of 130 flight sequences for a commercial quadcopter platform. This dataset includes different flight paths, fault types, and severity levels. The dataset primarily comprises onboard sensor readings from the drone and the corresponding mission trajectory logs. All faults develop while the drone operates normally, with no significant impact on performance across flight phases. These faults include minor cracks, edge damage, and holes, which are some of the popular propeller failures. This dataset is valuable for exploring effective fault detection methods and predictive maintenance capabilities. |
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| ISSN: | 2352-3409 |