用于数据驱动时间序列分析的单变压力曲线的数据集
Bernd Hofmann1, Patrick Bründl2, Jörg Franke2
1Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Factory Automation and Production Systems (FAPS), Nuremberg, Germany. bernd.hofmann@faps.fau.de.
Scientific data
|August 26, 2025
概括
这项研究为制造质量控制引入了2,439个曲线的新数据集. 这种资源有助于开发机器学习模型和时间序列分析以改善工业流程.
科学领域:
- 制造工程
- 数据科学
- 质量控制
背景情况:
- 数据可用性是制造业开发数据驱动工具的一个重大挑战.
- 接在工业环境中至关重要,需要严格的质量控制.
- 目前分析缩力数据的方法有限.
研究的目的:
- 引入用于制造应用的曲力曲线的全新,全面数据集.
- 促进针对特定领域的机器学习模型的开发.
- 提供用于工业质量控制的单变时间序列分析的基准数据集.
主要方法:
- 从一个半自动缩机收集了2,439个缩力曲线.
- 使用最先进的异常检测系统进行注释曲线.
- 将曲线分为多个质量类别提供详细的手动分类.
主要成果:
- 现在可以获得2,439个注释曲线的独特数据集.
- 该数据集包括自动异常检测和详细的手动质量分类.
- 这为制造业的机器学习和时间序列分析提供了便利.
结论:
- 这一数据集解决了制造业对数据可用性的关键需求.
- 它作为一个有价值的资源,用于推进数据驱动的质量控制.
- 进一步研究和开发智能制造系统.
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