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A Manufacturing Big Data Solution for Active Preventive Maintenance

IEEE Transactions on Industrial Informatics · 2017 · Vol. 13(4) · pp. 2039–2047
Jiafu WanShenglong TangDi LiShiyong WangChengliang LiuHaider AbbasAthanasios V. Vasilakos

Abstract

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.

Digital Transformation in IndustryIndustrial Vision Systems and Defect DetectionBig Data and Business IntelligencePreventive maintenanceCloud computingBig dataPredictive maintenanceComputer scienceCyber-physical systemArchitectureManufacturingManufacturing engineeringEngineering

Funding

  • National Natural Science Foundation of China
  • Natural Science Foundation of Guangdong Province
  • National Key Research and Development Program of China
  • Fundamental Research Funds for the Central Universities
Citations
430
FWCI
60.02
field-weighted impact
References
21
Percentile
100%
vs. same field & year
Citations per year
References
State-of-the-Art Predictive Maintenance Techniques*
IEEE Transactions on Instrumentation and Measurement · 2011 · 347 citations
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