A Cloud-Edge Collaborative Soft Sensing Framework for Multiperformance Indicators of Manufacturing Processes With Irregular Sampling

被引：0
|
作者：
Xu, Qingquan [1 ]
Dong, Jie [1 ]
Peng, Kaixiang [1 ]
Zhang, Qichun [1 ]
机构：
[1] University of Science and Technology Beijing, School of Automation and Electrical Engineering, Beijing,100083, China
来源：
IEEE Transactions on Instrumentation and Measurement | 2024年 / 73卷
基金：
中国国家自然科学基金;
关键词：
Adaptive boosting - Smart manufacturing - Strip metal;
D O I：
10.1109/TIM.2024.3488152
中图分类号：
学科分类号：
摘要：
In the process industry production, the online sensing of process performance is very important for the optimization and control of the manufacturing process. However, the information island is formed by long processes and multiple systems of complex production processes. The process data are characterized by high dimensional heterogeneity, nonlinearity, and strong coupling, and the offline assay of process performance is characterized by high discretization and irregular sampling period. In order to solve the above problems, a cloud-edge collaborative soft sensing framework for multiperformance indicators prediction of manufacturing processes with nonregular sampling is proposed. Also, some experiments are carried out with the actual hot strip rolling process, which realizes the joint real-time sensing of the three performance indicators of yield strength (YS), tensile strength (TS), and elongation (EL) with good accuracy. © 2024 IEEE.
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