工业级电机在随机转速和负载变化下的多重故障条件下的振动,电流,扭矩,转速数据集
Wonho Jung1, Junho Kim1, Kangmin Jang1
1Center for Noise and Vibration Control Plus, Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291, Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea.
这项研究引入了用于训练工业交流电机和HVAC单元故障诊断的机器学习模型的大型多样化数据集. 它在可变的运行条件下具有多故障,多重度的条件,以实现强大的预测性维护.
科学领域:
- 机械工程
- 电气工程
- 数据科学
背景情况:
- 开发强大的故障诊断算法对于工业机械的可靠性至关重要.
- 现有的数据集往往缺乏现实世界操作条件的复杂性,限制了模型的概括性.
- 以数据为导向的方法需要全面的标记数据集以进行有效的培训和验证.
研究的目的:
- 提供一个全面的,多条件数据集用于交流电机和HVAC单元的故障诊断.
- 支持基于数据的故障检测和预测性维护方法的开发和验证.
- 解决现有数据集的局限性,包括多故障,多重度和可变的操作条件.
主要方法:
- 模拟故障的工业交流电机收集的数据 (绕,短路,错位,轴承问题) 在多个严重程度.
- 在不同的带张力下遇到带松动故障的HVAC单元获得的数据.
- 在随机的速度和负载变化下记录的多传感器信号,包括电流,振动,扭矩和RPM.
主要成果:
- 一个大数据集 (>60 GB) 的原始传感器信号,详细的元数据和故障类型,严重程度和操作条件的标签.
- 在可变速度 (6%,16%) 和负载条件下收集数据,模拟工业环境.
- 该数据集包括多种故障场景,使得可通用诊断算法的训练成为可能.
结论:
- 这一数据集填补了开发强大,适用于现实世界的故障诊断算法的关键缺口.
- 它适用于评估机器学习模型和信号处理技术,用于状态监测和预测性维护.
- 该数据集为学术研究和工业实施提供了监督学习应用.
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