一种基于串行并行GRU的新软传感方法,用于复杂的多单元工业过程
Kaixiang Peng1, Guanyao Wang2, Tie Li2
1Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, PR China; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China.
ISA transactions
|September 6, 2025
概括
这项研究引入了一种新的软传感器模型 (SPGRU-SA) 用于预测制造业关键性能指标 (KPIs). 它准确地预测复杂工业过程中的KPI,克服传统测试方法的局限性.
科学领域:
- 优化制造过程
- 工业中的人工智能
- 软传感器技术
背景情况:
- 传统的关键绩效指标 (KPI) 测试是耗时且昂贵的.
- 数字化转型需要及时准确地预测制造业的KPI.
- 现有的方法无法提供有效的实时生产指导.
研究的目的:
- 开发一个先进的软传感器模型用于在线KPI预测.
- 解决传统的破坏性检测方法的局限性.
- 提高工业过程中KPI监测的效率和准确性.
主要方法:
- 提出了一种新的串行并行门式反复单元与自我注意 (SPGRU-SA) 软传感器模型.
- 使用SPGRU从多单元过程中提取动态特征.
- 使用自我注意机制来衡量相关性分析的静态和动态特征.
主要成果:
- SPGRU-SA模型证明了KPI的准确在线预测.
- 有效地捕获动态和静态过程特征.
- 在热卷机和田纳西东曼工艺上验证了性能.
结论:
- 在复杂的多个单位的工业环境中,SPGRU-SA准确地预测了KPI.
- 这种模式可以替代昂贵而缓慢的传统测试.
- 增强实时决策并减少制造损失.
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