电机电流和振动监测数据集用于E电机驱动的离心中的各种故障
S Bruinsma1,2, R D Geertsma3, R Loendersloot2
1Royal Netherlands Navy, Den Helder, The Netherlands.
Data in brief
|December 28, 2023
概括
这项研究提供了一个全面的数据集,用于诊断感应电机故障. 这些数据使先进的机器学习能够用于早期故障检测,并改善工业和海军应用中的操作可用性.
科学领域:
- 机械工程 机械工程
- 电气工程 电气工程
- 数据科学数据科学数据科学
背景情况:
- 感应电机驱动的在工业和海上应用中至关重要.
- 部件退化,安装错误或使用问题可能导致故障,导致停机时间和潜在的附带损害.
- 使用动态响应测量的状态监控对于优化可用性和防止故障至关重要.
研究的目的:
- 创建一个有价值的实验数据集,用于开发和验证感应电机驱动的故障检测,识别和诊断方法.
- 为研究人员和工程师提供一个资源,以旋转机械诊断工作.
- 促进机器学习和人工智能算法的培训和开发,用于条件监测.
主要方法:
- 从通过变频驱动器驱动的感应电机驱动的两个离心收集实验数据.
- 实施各种模拟故障,包括轴承缺陷,螺旋损坏,定子绕短线,旋转杆问题,软脚,不对齐,不平衡,合降解,空洞化和曲轴.
- 在健康和故障条件下记录振动测量和电机电流和电压测量,跨多个严重程度和电机转速.
主要成果:
- 一个全面的数据集,包括健康运行和广泛的模拟故障,具有多种严重程度和电机速度.
- 高质量,广泛记录和准确标记的数据适合机器学习和AI模型训练.
- 同时记录每个实验案例的振动,电流和电压数据.
结论:
- 本文所介绍的数据集是推动旋转机械状态监测和故障诊断领域的宝贵资源.
- 数据的质量和广度支持开发强大的AI驱动的诊断工具.
- 该资源将有助于提高关键系统的可靠性和可用性.
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