Construction and evaluation method of unmanned aerial vehicle faults simulation dataset

Wang Yicheng; Chai Mengjuan; Yu Daojie; Bai Yijie; Liang Liyue; Li Tao; Zhou Jiale; Du Jianping; Yao Zhenning

doi:10.11884/HPLPB202537.240340

Volume 37 Issue 4

Mar. 2025

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Wang Yicheng, Chai Mengjuan, Yu Daojie, et al. Construction and evaluation method of unmanned aerial vehicle faults simulation dataset[J]. High Power Laser and Particle Beams, 2025, 37: 043006. doi: 10.11884/HPLPB202537.240340

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Wang Yicheng, Chai Mengjuan, Yu Daojie, et al. Construction and evaluation method of unmanned aerial vehicle faults simulation dataset[J]. High Power Laser and Particle Beams, 2025, 37: 043006. doi: 10.11884/HPLPB202537.240340

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Construction and evaluation method of unmanned aerial vehicle faults simulation dataset

doi: 10.11884/HPLPB202537.240340

School of Information Systems Engineering, Information Engineering University, Zhengzhou 450002, China

Received Date: 2024-09-23
Accepted Date: 2024-12-11
Rev Recd Date: 2024-12-11

Available Online: 2024-12-20

Publish Date: 2025-04-15

Abstract

Abstract

The complexity of unmanned aerial vehicle (UAV) systems and the diversity of their fault modes present significant challenges to their reliability, stability, and safety. To address the issue of incomplete fault UAV data samples, a fault simulation dataset was constructed using a predefined fault injection method. This dataset is based on four models of faults: bias faults, drift faults, lock faults, and scale faults, allowing equivalent simulation of the drone in fault-free states, actuator failures, and sensor failures. Furthermore, the dataset was evaluated using deep learning networks. Simulation results demonstrate that the three deep learning architectures—WDCNN, ResNet, and QCNN—validate the completeness and effectiveness of the construction method and the fault simulation dataset in this paper. In terms of precision, WDCNN achieved over 82%, ResNet exceeded 90%, and QCNN surpassed 92%. The methods proposed in this study provides a complete dataset and evaluation method for data-driven research on UAV fault diagnosis.
- fault diagnosis,
- unmanned aerial vehicle system,
- fault dataset,
- data driven,
- deep learning

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References(26)

References

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