Multiaxial vibration data for blade fault diagnosis in multirotor unmanned aerial vehicles

The complete dataset consists of five Excel files, each corresponding to a specific flight condition of the UAV, including both healthy and faulty cases. These cases involve controlled modifications to the UAV's propeller blades, such as mass removal (to simulate damage) and weight addition using adhesive tape (to induce imbalance). The dataset was collected under repeatable indoor hover flight conditions, using a centrally mounted ADXL335 triaxial accelerometer connected to a NI USB-6008 DAQ system. Each file contains approximately 400,000 rows of raw, high-resolution vibration signals recorded continuously over a 7-minute period. The files are named clearly to reflect the specific fault case they represent:
• Healthy
• Damaged Bottom Right Blade
• Damaged Top Right Blade
• Unbalanced Bottom Right Blade
• Unbalanced Top Right Blade
Each Excel file includes four columns, structured as follows:
• Column A: Time (in seconds)
• Column B: X-axis acceleration (m/s²)
• Column C: Y-axis acceleration (m/s²)
• Column D: Z-axis acceleration (m/s²)
Manual labeling was used to ensure maximum clarity, with filenames explicitly indicating the fault condition and blade position. All data are recorded in the time domain, suitable for transformation into frequency or time-frequency domains depending on user needs. These characteristics make the dataset especially useful for UAV diagnostics, machine learning classification tasks, and vibration signal analysis.

完整数据集包含五个Excel文件，每个文件对应无人机的一种特定飞行工况，涵盖健康与故障两种状态。这些工况通过对无人机螺旋桨桨叶进行可控改装实现，具体包括移除桨叶质量以模拟桨叶损坏，以及通过粘贴胶带添加重量以引入系统不平衡。该数据集在可重复的室内悬停飞行工况下采集，使用安装于机身中央的ADXL335三轴加速度传感器，连接至NI USB-6008数据采集（Data Acquisition, DAQ）系统。每个文件包含约40万行原始高分辨率振动信号数据，连续采集时长为7分钟。

文件命名清晰直观，可直接反映其所代表的具体故障工况：
• 健康状态
• 右下桨叶损坏
• 右上桨叶损坏
• 右下桨叶不平衡
• 右上桨叶不平衡

每个Excel文件包含四列数据，结构如下：
• 列A：时间（单位：秒）
• 列B：X轴加速度（单位：m/s²）
• 列C：Y轴加速度（单位：m/s²）
• 列D：Z轴加速度（单位：m/s²）

本次采集采用人工标注以确保最高标注清晰度，文件名已明确标注故障工况与对应桨叶位置。所有数据均为时域记录格式，可根据用户需求转换至频域或时频域。上述特性使得该数据集尤其适用于无人机故障诊断、机器学习分类任务以及振动信号分析研究。