Al-Haddad Luttfi A, Jaber Alaa Abdulhady, Hamzah Mohsin N, Kraiem Habib, Al-Karkhi Mustafa I, Flah Aymen
Mechanical Engineering Department, University of Technology- Iraq, Baghdad, Iraq.
Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia.
Sci Data. 2025 Aug 7;12(1):1383. doi: 10.1038/s41597-025-05692-4.
This dataset presents multiaxial vibration signals collected from a multirotor unmanned aerial vehicle (UAV) operating in hover mode for the purpose of blade fault diagnosis. Vibration measurements were recorded at the geometric center of the UAV, where the centerlines of the four rotor arms intersect, using a triaxial accelerometer. The dataset captures variations across the X, Y, and Z axes under different blade fault conditions, including healthy, minor imbalance, severe imbalance, and screw loosening scenarios. Each flight scenario was repeated under controlled conditions to ensure consistency and high-quality labeling. The resulting soft-labeled dataset includes time-domain signals from numerous test flights and has been used in multiple prior studies involving classical and deep learning-based fault classification techniques. This curated data collection provides a valuable resource for researchers in UAV health monitoring, vibration analysis, and machine learning-based fault diagnosis. The dataset is particularly useful for the development and benchmarking of signal processing pipelines and classification models aimed at identifying blade-level faults in multirotor UAV systems.
该数据集呈现了从处于悬停模式的多旋翼无人机收集的多轴振动信号，用于叶片故障诊断。使用三轴加速度计在无人机的几何中心（四个旋翼臂的中心线相交处）记录振动测量数据。该数据集捕捉了不同叶片故障条件下（包括健康、轻微不平衡、严重不平衡和螺丝松动情况）X、Y和Z轴上的变化。每个飞行场景在受控条件下重复进行，以确保一致性和高质量标注。所得的软标注数据集包括来自多次试飞的时域信号，并已用于涉及基于经典和深度学习的故障分类技术的多项先前研究中。这个经过整理的数据收集为无人机健康监测、振动分析以及基于机器学习的故障诊断领域的研究人员提供了宝贵资源。该数据集对于旨在识别多旋翼无人机系统叶片级故障的信号处理管道和分类模型的开发及基准测试特别有用。
