SCANIA X组件数据集:用于预测性维护的现实世界多变量时间序列数据集
Zahra Kharazian1, Tony Lindgren2,3, Sindri Magnússon2
1Stockholm University, Department of Computer and Systems Sciences, Kista, SE-164 07, Sweden. zahra.kharazian@dsv.su.se.
Scientific data
|March 25, 2025
概括
这项研究引入了一个新的,现实世界的多变量时间序列数据集,用于预测性维护. 它解决了数据稀缺问题,使高级机器学习能够用于组件故障预测.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 预测性维护依赖于全面的数据集,这些数据很少,特别是时间序列格式.
- 现有的现实数据用于预测元件故障是有限的,阻碍了研究和开发.
研究的目的:
- 为预测性维护应用引入一个独特的,现实世界的多变量时间序列数据集.
- 为预测性维护领域提供标准化的基准,以促进可重现的研究.
主要方法:
- 从SCANIA卡车车队收集运营数据,维修记录和部件规格.
- 来自单个发动机组件 (组件X) 的数据的匿名化,以确保保密性.
- 包括各种功能,如直方图和数值计数器,以及时间信息.
主要成果:
- 一个全面的,现实世界的多变量时间序列数据集,专门用于X组件.
- 该数据集适用于各种机器学习任务:分类,回归,生存分析和异常检测.
- 使用来自主要汽车制造商的真实世界数据进行研究.
结论:
- 发布的数据集解决了在预测性维护研究中对现实世界数据的关键需求.
- 该资源将使研究人员能够开发和验证先进的机器学习模型,以改进组件故障预测.
- 建立了预测性维护领域未来研究的基准.
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