A Manufacturing Big Data Solution for Active Preventive Maintenance

被引:303
作者
Wan, Jiafu [1 ]
Tang, Shenglong [2 ]
Li, Di [2 ]
Wang, Shiyong [2 ]
Liu, Chengliang [3 ]
Abbas, Haider [4 ,5 ,6 ]
Vasilakos, Athanasios V. [7 ]
机构
[1] South China Univ Technol, Guangdong Prov Key Lab Precis Equipment & Mfg Tec, Guangzhou 510641, Guangdong, Peoples R China
[2] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China
[3] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China
[4] King Saud Univ, Ctr Excellence Informat Assurance, Riyadh 999088, Saudi Arabia
[5] Natl Univ Sci & Technol, Islamabad 44000, Pakistan
[6] Florida Inst Technol, Melbourne, FL 32901 USA
[7] Lulea Univ Technol, S-97187 Lulea, Sweden
基金
中国国家自然科学基金;
关键词
Big data; cyber-physical systems; Industry; 4.0; intelligent manufacturing; preventive maintenance; DATA-COLLECTION;
D O I
10.1109/TII.2017.2670505
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.
引用
收藏
页码:2039 / 2047
页数:9
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