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A new paradigm of cloud-based predictive maintenance for intelligent manufacturing

Author

Listed:
  • Jinjiang Wang

    (China University of Petroleum)

  • Laibin Zhang

    (China University of Petroleum)

  • Lixiang Duan

    (China University of Petroleum)

  • Robert X. Gao

    (University of Connecticut)

Abstract

Advances in cloud computing reshape the manufacturing industry into dynamically scalable, on-demand service oriented, and highly distributed cost-efficient business model. However it also poses challenges such as reliability, availability, adaptability, and safety on machines and processes across spatial boundaries. To address these challenges, this paper investigates a cloud-based paradigm of predictive maintenance based on mobile agent to enable timely information acquisition, sharing and utilization for improved accuracy and reliability in fault diagnosis, remaining service life prediction, and maintenance scheduling. In the new paradigm, a low-cost cloud sensing and computing node is firstly developed with embedded Linux operating system, mobile agent middleware, and open source numerical libraries. Information sharing and interaction is achieved by mobile agent to distribute the analysis algorithms to cloud sensing and computing node to locally process data and share analysis results. Comparing to the commonly used client–server paradigm, the mobile agent approach enhances the system flexibility and adaptability, reduces raw data transmission, and instantaneously responds to dynamic changes of operations and tasks. Finally, the presented cloud-based paradigm of predictive maintenance is validated on a motor tested system.

Suggested Citation

  • Jinjiang Wang & Laibin Zhang & Lixiang Duan & Robert X. Gao, 2017. "A new paradigm of cloud-based predictive maintenance for intelligent manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1125-1137, June.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:5:d:10.1007_s10845-015-1066-0
    DOI: 10.1007/s10845-015-1066-0
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    Citations

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    Cited by:

    1. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    2. Haibo Yi, 2021. "A post-quantum secure communication system for cloud manufacturing safety," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 679-688, March.
    3. Neeraj Gupta & Saurabh Gupta & Mahdi Khosravy & Nilanjan Dey & Nisheeth Joshi & Rubén González Crespo & Nilesh Patel, 2021. "RETRACTED ARTICLE: Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1117-1128, April.
    4. Rui Liu, 2023. "An edge-based algorithm for tool wear monitoring in repetitive milling processes," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2333-2343, June.
    5. Jens Passlick & Sonja Dreyer & Daniel Olivotti & Lukas Grützner & Dennis Eilers & Michael H. Breitner, 2021. "Predictive maintenance as an internet of things enabled business model: A taxonomy," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 67-87, March.

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