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The increase in the amount of manufacturing information available means that big data can be collected and, with appropriate deep analysis, could be of great value to manufacturers. However, most small manufacturers cannot afford the overhead of a professional data analytics team. To address this problem, in this paper a generic data analytics system, Generic Manufacturing Data Analytics system (GMDA), is proposed. This system can perform most manufacturing data analytics tasks and users can easily carry out data analysis even if they have no prior knowledge or experience of data analytics. To establish such a system, we designed an abstract language, GMDL, to describe the manufacturing data analytics tasks. Aimed at factory data analytics, several algorithms were selected, tuned, optimized, and finally integrated into the system. Some noteworthy techniques were developed in GMDA such as proper algorithm selection strategy and an optimal parameter determination algorithm. Case studies show the practicability and reliability of the system.


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A Generic Data Analytics System for Manufacturing Production

Show Author's information Hao ZhangHongzhi Wang( )Jianzhong LiHong Gao
Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.

Abstract

The increase in the amount of manufacturing information available means that big data can be collected and, with appropriate deep analysis, could be of great value to manufacturers. However, most small manufacturers cannot afford the overhead of a professional data analytics team. To address this problem, in this paper a generic data analytics system, Generic Manufacturing Data Analytics system (GMDA), is proposed. This system can perform most manufacturing data analytics tasks and users can easily carry out data analysis even if they have no prior knowledge or experience of data analytics. To establish such a system, we designed an abstract language, GMDL, to describe the manufacturing data analytics tasks. Aimed at factory data analytics, several algorithms were selected, tuned, optimized, and finally integrated into the system. Some noteworthy techniques were developed in GMDA such as proper algorithm selection strategy and an optimal parameter determination algorithm. Case studies show the practicability and reliability of the system.

Keywords:

manufactory, data analytics, data mining, optimization
Received: 12 January 2018 Accepted: 17 January 2018 Published: 12 April 2018 Issue date: June 2018
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Publication history

Received: 12 January 2018
Accepted: 17 January 2018
Published: 12 April 2018
Issue date: June 2018

Copyright

© The author(s) 2018

Acknowledgements

This paper was partially supported by the National Natural Science Foundation of China (Nos. U1509216, 61472099, and 61602129), the National Key Research and Development Program of China (No. 2016YFB1000703), National Sci-Tech Support Plan (No. 2015BAH10F01), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Heilongjiang Provience (No. LC2016026).

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