统计过程控制的机器学习中的数学和算法进步:系统审查
Yulong Qiao1, Tingting Han2,3, Zixing Wu1
1School of Information Technology, Jiangsu Open University, Nanjing 210036, China.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
本综述综合了工业4.0中的统计过程控制 (SPC) 机器学习 (ML). 它解决了复杂的制造数据挑战,如高维度和不平衡,指导ML技术选择进行强大的监控.
科学领域:
- 工业工程 工业工程 工业工程
- 数据科学数据科学数据科学
- 制造系统制造系统的制造
背景情况:
- 工业4.0制造产生复杂的数据 (高维,自相关,非静止,不平衡).
- 经典的统计过程控制 (SPC) 方法与这些数据特征作斗争.
- 机器学习 (ML) 为高级SPC提供了潜在的解决方案.
研究的目的:
- 系统地审查和合成工业4.0.0中SPC的ML技术.
- 将制造业中的特定数据挑战与适当的ML方法联系起来.
- 为选择和部署基于ML的SPC系统提供指导.
主要方法:
- 按照PRISMA 2020指南进行系统的文献审查.
- 由问题驱动的综合,根据数据挑战 (维度,自相关性,不平衡) 将ML方法分类.
- 复习数学推理和代表算法的工业应用.
主要成果:
- 对于高维数据的ML方法包括缩小维度和特征选择.
- 时间序列和状态空间模型处理自相关和动态过程.
- 成本敏感的学习,生成模型和转移学习解决了数据稀缺和不平衡的问题.
结论:
- 为复杂的制造数据选择ML技术提供了结构化的指导.
- 审查强调了ML-SPC的可解释性,值和实时部署方面的悬而未决的问题.
- 这项工作有助于为工业4.0.0设计可靠的在线监控管道.
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