Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University（工业控制技术国家重点实验室，控制科学与工程学院，浙江大学）；Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems（复杂系统先进控制与智能自动化湖北省重点实验室）；Department of Chemical and Materials Engineering, University of Alberta（阿尔伯塔大学化学与材料工程系）
For large-scale industrial processes under closed-loop control, process dynamics directly resulting from control action are typical characteristics and may show different behaviors between real faults and normal changes of operating conditions. However, conventional distributed monitoring approaches do not consider the closed-loop control mechanism and only explore static characteristics, which thus are incapable of distinguishing between real process faults and nominal changes of operating conditions, leading to unnecessary alarms. In this regard, this paper proposes a distributed monitoring method for closed-loop industrial processes by concurrently exploring static and dynamic characteristics. First, the large-scale closed-loop process is decomposed into several subsystems by developing a sparse slow feature analysis (SSFA) algorithm which capture changes of both static and dynamic information. Second, distributed models are developed to separately capture static and dynamic characteristics from the local and global aspects. Based on the distributed monitoring system, a two-level monitoring strategy is proposed to check different influences on process characteristics resulting from changes of the operating conditions and control action, and thus the two changes can be well distinguished from each other. Case studies are conducted based on both benchmark data and real industrial process data to illustrate the effectiveness of the proposed method.
A novel method for error analysis in radiation thermometry with application to industrial furnaces
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA)（Vicomtech基金会，巴斯克研究与技术联盟）；TECNUN School of Engineering, University of Navarra（纳瓦拉大学TECNUN工程学院）；Institute of Data Science and Artificial Intelligence, University of Navarra（纳瓦拉大学数据科学与人工智能研究所）；Petronor Innovation（Petronor创新公司）；Petróleos del Norte（北方石油公司）
TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine
Tim Langer Chair of Highly-Parallel VLSI Systems and Neuro-Microelectronics TU Dresden（高等并行非常规集成电路系统与神经微电子学教授，德累斯顿技术大学）；Fraunhofer Institute for Machine Tools and Forming Technology IWU Dresden（机械加工与成形技术弗劳恩霍夫研究所，德累斯顿）；Fraunhofer Institute for Integrated Circuits IIS Division Engineering of Adaptive Systems EAS Dresden（集成电路弗劳恩霍夫研究所，适应性系统工程部门，德累斯顿）
Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
Department of Electrical and Computer Engineering, University of Victoria（电气与计算机工程系，维多利亚大学）；Department of Mechanical Engineering, University of Victoria（机械工程系，维多利亚大学）
Experimental Investigation of Control Updating Period Monitoring In Industrial PLC-based Fast MPC: Application to The Constrained Control of a Cryogenic Refrigerator
CNRS, Control Systems Department, University of Grenoble（法国国家科学研究中心，控制系统系，格勒诺布尔大学）
The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments
Northern Arizona University（北亚利桑那大学）；University of Electronic Science and Technology of China（电子科技大学）；Trine University（特莱恩大学）；Yantai University（烟台大学）；Northwestern University（西北大学）
