一种基于双层图形监督嵌入的新型质量预测模型,采用多颗粒度的注意力增强机制
Jianing Hou1, Tie Li2, Kaixiang Peng1
1Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
ISA transactions
|August 9, 2025
概括
本研究引入了一种新的制造质量预测模型 (DGS-MA),通过使用双层图形结构和注意力机制来提高准确性,以更好地捕捉复杂的工业数据关系.
科学领域:
- 工业过程监控 工业过程监控
- 机器学习用于制造业
- 数据驱动的质量控制
背景情况:
- 传统的软传感与复杂的工业数据作斗争,包括非线性相互作用和动态变化.
- 现有的方法往往无法充分表示多变量合,限制了预测准确度.
- 适应不断变化的工业环境仍然是当前质量预测模型面临的重大挑战.
研究的目的:
- 开发一个先进的制造质量预测模型,解决传统软传感的局限性.
- 增强工业过程数据中复杂关系的表示.
- 在动态的工业环境中提高质量预测的适应性和准确性.
主要方法:
- 提出了一种双层图形监督嵌入与多颗粒度注意力增强机制 (DGS-MA) 模型.
- 构建了一个双层图:局部协会的特征相似度图和全球拓的监督Node2vec图.
- 实施了多粒度图的注意力机制,用于特征融合和明确监督约束的双路径和跨层注意力.
主要成果:
- DGS-MA模型在质量预测准确度方面取得了显著的改进.
- 通过双视图表示,有效地捕获了本地静态协会和全球质量驱动拓.
- 注意增强机制成功地将邻里信息从原始特征和监督嵌入中融合在一起.
- 明确的监督约束提高了预测准确性和模型可解释性.
结论:
- 在复杂的工业环境中,DGS-MA模型为数据驱动的质量预测提供了一个强大的解决方案.
- 双层图形和多粒度注意力的集成显著优于传统方法.
- 这种方法为工业过程质量监测提供了一个更准确,更易于解释的框架.
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