球轴承的振动和温度运行到故障数据集用于预测
Wonho Jung1, Sung-Hyun Yun1, Yong-Hwa Park1
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, Republic of Korea.
Data in brief
|April 25, 2024
概括
这项研究提供了一个新的球轴承运行到故障数据数据集,包括振动和温度,以帮助预测剩余使用寿命 (RUL). 本资源支持开发基于条件的先进维护 (CBM) 策略.
科学领域:
- 机械工程 机械工程
- 可靠性工程可靠性工程
- 数据科学数据科学数据科学
背景情况:
- 基于条件的维护 (CBM) 通过预测零件故障,对工业库存管理至关重要.
- 准确预测轴承寿命需要在整个运行寿命内提供全面的数据.
研究的目的:
- 在运行到故障条件下呈现球轴承振动和温度数据的时间序列数据集.
- 为开发和测试剩余使用寿命 (RUL) 预后方法提供一个验证的数据集.
主要方法:
- 在128个工作小时后,在一个球轴承上进行了加速寿命测试,直到故障.
- 在整个操作期间收集振动和温度数据.
- 进行了显微镜分析,以确定受损轴承的故障模式.
主要成果:
- 在加速寿命测试期间,成功获得了振动和温度的完整时间序列数据集.
- 数据集捕捉了球轴承的全部运行寿命,直到它的故障点.
- 特定的故障类型通过详细的显微镜检查来确定.
结论:
- 生成的数据集对于验证CBM中剩余使用寿命 (RUL) 预测的新算法是有价值的.
- 这一资源将推动开发更准确,更可靠的预测性维护技术.
- 该数据集促进了机器健康监测和预测方面的研究.
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