使用实验设计和基于随机监督时间序列森林和递归特征消除的机器学习方法监测流形成过程
Leroy Anozie1, Bodo Fink2, Christoph M Friedrich1,3
1Department of Computer Science, University of Applied Sciences and Arts (FH Dortmund), 44227 Dortmund, Germany.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
机器学习和传感器数据可以监控金属流形成过程,检测空白和机器中的缺陷. 这种数据驱动的方法为制造业的质量控制提供了实际的解决方案.
科学领域:
- 制造业 工程 制造工程
- 材料科学 材料科学 材料科学
- 数据科学与机器学习
背景情况:
- 流成型工艺对于金属工件制造至关重要,但质量在很大程度上取决于空白和机器状况.
- 由于过程复杂性,现有的物理建模不足,需要自动化监控解决方案.
- 对于实时监控来确保工件质量和机器健康,在流成型操作中非常需要.
研究的目的:
- 展示使用机器学习 (ML) 和传感器数据来监控流形成过程的可行性.
- 开发一种实用,数据驱动的方法来检测金属空白和机器条件中的缺陷.
- 创建适用于多变量时间序列分类的新型特征提取算法.
主要方法:
- 利用实验设计 (DOE) 进行高效的实验设计,优化从有限的试验收集数据.
- 开发了一个数据预处理管道,包括特征工程,一种基于r-STSF的多变量时间序列的新型特征提取,以及用于特征选择的递归特征消除 (RFE).
- 训练随机森林模型使用提取的特征来预测空白质量和机器缺陷.
主要成果:
- 开发的ML模型对大多数目标变量实现了良好的预测准确度,表明成功检测了缺陷.
- 新的特征提取算法证明在特定应用之外的多变量时间序列分类中是有效的.
- 数据驱动的方法成功地确定了空白和机器缺陷的质量特征.
结论:
- 机器学习,结合传感器数据和强大的功能工程,是监控复杂的流形成过程的非常有前途的方法.
- 开发的方法为金属成型中自动化质量控制提供了实际框架.
- 需要进一步的研究来充分验证和在工业环境中实施这种基于机器学习的监控系统.
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