单个和双重故障场景的数据集,使用来自旋转机器的振动信号
Larry Marshall1, David Jensen1
1University of Arkansas, Fayetteville, Arkansas 72701, USA.
Data in brief
|July 17, 2023
概括
这个数据集提供了来自旋转机器故障模拟器的振动传感器数据,对于训练机器学习模型来检测轴承和轴心故障非常有用. 数据涵盖了不同运行速度的各种故障场景.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 旋转机器容易出现各种故障,包括轴承缺陷和轴心问题.
- 早期故障检测对于防止灾难性故障和尽量减少停机时间至关重要.
- 振动分析是一种常见的技术,用于监测旋转机械的健康状况.
研究的目的:
- 来自旋转机器故障模拟器的振动传感器数据创建一个全面的数据集.
- 为开发和验证用于故障分类的机器学习算法提供数据.
- 包括特定故障类型的数据,如内外赛道轴承故障和曲轴.
主要方法:
- 使用安装在支轴承上的加速度计收集振动数据.
- 在受控条件下,从故障模拟器机器记录数据.
- 故障场景包括38个单一和双重故障条件,以及三种操作频率的健康状态.
- 数据采集以6400 Hz的采样速率进行,每个场景大约10秒.
主要成果:
- 成功生成了一组包含各种已知的故障条件的振动信号的数据集.
- 数据集包括一系列故障严重程度和类型,包括轴承跑道和轴心缺陷.
- 数据被收集在多个操作速度,提供不同的操作环境.
结论:
- 生成的数据集适用于基于机器学习的旋转机械故障诊断.
- 这个资源可以帮助研究人员开发更强大,更准确的预测性维护系统.
- 这些数据有助于研究与特定机械故障相关的振动模式.
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