Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance

Industry 4.0 can make a factory smart by applying intelligent information processing approaches, communication systems, future-oriented techniques, and more. However, the high complexity, automation, and flexibility of an intelligent factory bring new challenges to reliability and safety. Industrial...

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Bibliographic Details
Published in:IEEE access 2017-01, Vol.5, p.23484-23491
Main Authors: Yan, Jihong, Meng, Yue, Lu, Lei, Li, Lin
Format: Article
Language:English
Subjects:
Big Data
Communications systems
Data mining
Data models
Data processing
Feature extraction
Industrial applications
Industrial big data
Industries
Industry 4.0
Life prediction
Machining
Manufacturing processes
multiple invisible factors
multisource heterogeneous data
Plant reliability
Predictive maintenance
structuralization and characterization
System reliability
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Summary:Industry 4.0 can make a factory smart by applying intelligent information processing approaches, communication systems, future-oriented techniques, and more. However, the high complexity, automation, and flexibility of an intelligent factory bring new challenges to reliability and safety. Industrial big data generated by multisource sensors, intercommunication within the system and external-related information, and so on, might provide new solutions for predictive maintenance to improve system reliability. This paper puts forth attributes of industrial big data processing and actively explores industrial big data processing-based predictive maintenance. A novel framework is proposed for structuring multisource heterogeneous information, characterizing structured data with consideration of the spatiotemporal property, and modeling invisible factors, which would make the production process transparent and eventually implement predictive maintenance on facilities and energy saving in the industry 4.0 era. The effectiveness of the proposed scheme was verified by analyzing multisource heterogeneous industrial data for the remaining life prediction of key components of machining equipment.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2765544