Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era  

作　　者：Chao Shang Fengqi You 

机构地区：[1]Department of Automation,Tsinghua University,Beijing 100084,China [2]Robert Frederick Smith School of Chemical and Biomolecular Engineering,Cornell University,Ithaca,NY 14853,USA

出　　处：《Engineering》2019年第6期1010-1016,共7页工程（英文）

摘　　要：Safe, ef cient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is in uencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning mod- els. By analyzing the gap between practical requirements and the current research status, promising future research directions are identi ed.

关 键 词：Big data Machine learning Smart manufacturing Process systems engineering 

分 类 号：F41[经济管理—产业经济]