Scalable data pipeline architecture to support the industrial internet of things

被引：13

作者：

Helu, Moneer ^{[1
]}

Sprock, Timothy ^{[1
]}

Hartenstine, Daniel ^{[2
]}

Venketesh, Rishabh ^{[1
]}

Sobel, William ^{[3
]}

机构：

[1] NIST, Engn Lab, Gaithersburg, MD 20899 USA

[2] Millersville Univ Pennsylvania, Millersville, PA 17551 USA

[3] VIMANA, Oakland, CA 94612 USA

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2020年 / 69卷 / 01期

关键词：

System architecture; Standardization; Data;

D O I：

10.1016/j.cirp.2020.04.006

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Managing manufacturing data remains challenging despite the growth of the Industrial Internet of Things (IIoT). While various standards and technologies enable greater access to data, scaling data processing and distribution can be difficult given the increasing variety of data from an increasing variety of sources in global production networks. This paper proposes an architecture for a scalable pipeline to process and distribute data from a mix of shop-floor sources. The feasibility of this approach is explored by implementing the architecture to bring together MTConnect-compliant machine and ad-hoc power data to support analytics applications. Published by Elsevier Ltd on behalf of CIRP.

引用

页码：385 / 388

页数：4

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