Intelligent bearing fault diagnosis dataset

The experimental set of the faults simulator in the rotating machines made by the authors of the "Intelligent bearing fault diagnosis using swarm decomposition method and new hybrid particle swarm optimization algorithm" paper at Ahrar Institute of Technology and Higher Education (AITHE). This setup includes an electric motor of 0.5 hp, an inverter of 0.5 hp with the ability to change the speed between zero to 60 Hz, a belt-pulley set with a ratio of 2–1, a shaft with a length of 50 cm, and a diameter of 20 mm, two bearings, two anchor wheels for unbalancing, and a data logger. The accelerometer of the AC102-1A series and eight-channel data logger with the maximum sampling frequency of 250 kHz had been used to collect and record the vibration signals. The sensor used in this setup has a sensitivity of ± 50 g. The vibration data for different faults were obtained at the sampling rate of 17.85 kHz and the rotational speed of 1900 rpm. The bearings used in this system are of Nu 204 ECP type, the geometric specifications of which are presented in Fig. 18 (in the paper). The characteristics of the defects investigated in this experiment are described in Table 9 (in the paper). For further information read the paper (https://doi.org/10.1007/s00500-021-06307-x).

本实验数据集为阿赫拉理工高等研究院（Ahrar Institute of Technology and Higher Education, AITHE）发表的《基于群体分解法与新型混合粒子群优化算法的智能轴承故障诊断》论文作者所搭建的旋转机械故障模拟器实验台的相关数据集。该实验台包含一台0.5马力的电动机、一台可实现0至60Hz转速调节的0.5马力变频器、一组传动比为2:1的带轮组、一根长50cm、直径20mm的转轴、两个轴承、两个用于产生不平衡量的锚定轮，以及一台数据记录仪。实验采用AC102-1A系列加速度传感器与最大采样频率可达250kHz的八通道数据记录仪采集并记录振动信号，该传感器的灵敏度为±50g。本次实验以17.85kHz的采样率、1900rpm的旋转转速采集了不同故障类型下的振动数据。本实验台所用轴承型号为Nu 204 ECP，其几何规格详见论文中的图18。本实验所研究的缺陷特征详见论文中的表9。如需进一步了解相关细节，请参阅该论文（https://doi.org/10.1007/s00500-021-06307-x）。

搜集汇总

背景与挑战

背景概述

这是一个用于智能轴承故障诊断的数据集，基于旋转机械故障模拟实验采集的振动信号数据，采样率为17.85 kHz，转速为1900 rpm。数据集包含振动数据文件和目标标签文件，适用于机器学习或信号处理领域的故障诊断研究，并遵循CC BY 4.0许可证。