工业过程的机制和数据融合驱动的多指标软传感器框架
Qingquan Xu1, Jie Dong2, Kaixiang Peng2
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, PR China.
ISA transactions
|August 7, 2025
概括
这项研究引入了一种用于工业过程的新型软传感器框架,将机械和数据驱动模型融合在一起,以准确预测热带制中的条纹平度和冠状等质量指标.
科学领域:
- 工业过程控制 工业过程控制
- 软传感器技术 软传感器技术
- 机械工程 机械工程
背景情况:
- 传统的测量方法在工业质量控制方面存在局限性.
- 工业过程中的复杂机制和时间变化的延迟挑战了多指标软传感.
- 现有的研究缺乏软传感的机械和数据驱动模型的先进融合.
研究的目的:
- 提出一个机制和数据融合驱动的多指标软传感器框架.
- 为应对复杂工业过程的多指标软传感方面的挑战.
- 在工业环境中改进产品质量预测.
主要方法:
- 热流程 (HSRP) 的机制分析和未知参数的识别.
- 来自机械模型的数据与实际过程数据的融合.
- 开发具有嵌入式时间序列输入层 (TS-KAN) 的Kolmogorov-Arnold网络,以处理时间变化的延迟.
主要成果:
- 使用HSRP生产数据开发并验证了一种新的软传感器框架.
- 该框架成功地解决了复杂机制和不同时间的延迟所带来的挑战.
- 可以同时准确地预测条纹平度和冠状.
结论:
- 拟议的机制和数据融合框架为工业过程中的多指标软传感提供了强大的解决方案.
- 集成TS-KAN有效地管理时间变化的延迟,提高预测准确度.
- 这种方法显著改善了在热带工艺中的质量指标预测.
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