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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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S235234092500321X |
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| author | Mohamed A.A. Ismail Mohssen E. Elshaar Ayman Abdallah Quan Quan |
| author_facet | Mohamed A.A. Ismail Mohssen E. Elshaar Ayman Abdallah Quan Quan |
| author_sort | Mohamed A.A. Ismail |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fb9b2d6145e54b029c7e00fb4b4a6ecd |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-fb9b2d6145e54b029c7e00fb4b4a6ecd2025-08-20T03:25:05ZengElsevierData in Brief2352-34092025-06-016011158910.1016/j.dib.2025.111589DronePropA: Motion trajectories dataset for defective dronesMendeley DataMohamed A.A. Ismail0Mohssen E. Elshaar1Ayman Abdallah2Quan Quan3Interdisciplinary Research Center for Aviation and Space Exploration, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia; Aerospace Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia; Corresponding author.Interdisciplinary Research Center for Aviation and Space Exploration, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia; Aerospace Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaInterdisciplinary Research Center for Aviation and Space Exploration, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia; Aerospace Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, PR ChinaUnmanned 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.http://www.sciencedirect.com/science/article/pii/S235234092500321XHealth monitoringMulticopter dronePropeller cracksFault detectionUnbalance |
| spellingShingle | Mohamed A.A. Ismail Mohssen E. Elshaar Ayman Abdallah Quan Quan DronePropA: Motion trajectories dataset for defective dronesMendeley Data Data in Brief Health monitoring Multicopter drone Propeller cracks Fault detection Unbalance |
| title | DronePropA: Motion trajectories dataset for defective dronesMendeley Data |
| title_full | DronePropA: Motion trajectories dataset for defective dronesMendeley Data |
| title_fullStr | DronePropA: Motion trajectories dataset for defective dronesMendeley Data |
| title_full_unstemmed | DronePropA: Motion trajectories dataset for defective dronesMendeley Data |
| title_short | DronePropA: Motion trajectories dataset for defective dronesMendeley Data |
| title_sort | dronepropa motion trajectories dataset for defective dronesmendeley data |
| topic | Health monitoring Multicopter drone Propeller cracks Fault detection Unbalance |
| url | http://www.sciencedirect.com/science/article/pii/S235234092500321X |
| work_keys_str_mv | AT mohamedaaismail dronepropamotiontrajectoriesdatasetfordefectivedronesmendeleydata AT mohsseneelshaar dronepropamotiontrajectoriesdatasetfordefectivedronesmendeleydata AT aymanabdallah dronepropamotiontrajectoriesdatasetfordefectivedronesmendeleydata AT quanquan dronepropamotiontrajectoriesdatasetfordefectivedronesmendeleydata |