Plant-Wide Industrial Process Monitoring: A Distributed Modeling Framework

被引:138
作者
Ge, Zhiqiang [1 ]
Chen, Junghui [2 ]
机构
[1] Zhejiang Univ, Coll Control Sci & Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China
[2] Chung Yuan Christian Univ, Dept Chem Engn, Taoyuan 32023, Taiwan
基金
中国国家自然科学基金;
关键词
Decision fusion; distributed data framework; multirate data; multitype data; plant-wide process monitoring; CONTINUOUS ANNEALING PROCESSES; FAULT-DETECTION; COMPONENT ANALYSIS; MULTIBLOCK; DIAGNOSIS; PCA; PLS; SYSTEM;
D O I
10.1109/TII.2015.2509247
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the growing complexity of the modern industrial process, monitoring large-scale plant-wide processes has become quite popular. Unlike traditional processes, the measured data in the plant-wide process pose great challenges to information capture, data management, and storage. More importantly, it is difficult to efficiently interpret the information hidden within those data. In this paper, the road map of a distributedmodeling framework for plant-wide process monitoring is introduced. Based on this framework, the whole plant-wide process is decomposed into different blocks, and statistical data models are constructed in those blocks. For online monitoring, the results obtained from different blocks are integrated through the decision fusion algorithm. A detailed case study is carried out for performance evaluation of the plant-wide monitoring method. Research challenges and perspectives are discussed and highlighted for future work.
引用
收藏
页码:310 / 321
页数:12
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