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 c...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.展开更多
基金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)+1 种基金National Sci-Tech Support Plan (No. 2015BAH10F01)the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Heilongjiang Provience (No. LC2016026)
文摘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.
