面向未来智能工厂的大数据分析
Big data analytics for smart factories of the future.
作者信息
Gao Robert X, Wang Lihui, Helu Moneer, Teti Roberto
机构信息
Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, USA.
Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
出版信息
CIRP Ann Manuf Technol. 2020;9(2). doi: 10.1016/j.cirp.2020.05.002.
Continued advancement of sensors has led to an ever-increasing amount of data of various physical nature to be acquired from production lines. As rich information relevant to the machines and processes are embedded within these "big data", how to effectively and efficiently discover patterns in the big data to enhance productivity and economy has become both a challenge and an opportunity. This paper discusses essential elements of and promising solutions enabled by data science that are critical to processing data of high volume, velocity, variety, and low veracity, towards the creation of added-value in smart factories of the future.
传感器的不断进步使得从生产线上获取的各种物理性质的数据量不断增加。由于与机器和流程相关的丰富信息嵌入在这些“大数据”中，如何有效且高效地在大数据中发现模式以提高生产率和经济性已成为一项挑战和机遇。本文讨论了数据科学的关键要素和有前景的解决方案，这些对于处理高容量、高速度、多样且低准确性的数据至关重要，旨在为未来的智能工厂创造附加值。
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