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

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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.

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Acknowledgments

This research acknowledges the financial support provided by Science Foundation of China University of Petroleum, Beijing (No. 2462014YJRC039) and National Science foundation of China (No. 51204196). Support on design of cloud sensing and computing node in Michigan Technological University is appreciated. The valuable comments from anonymous reviewers are greatly acknowledged to help improve the paper’s quality.

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Authors and Affiliations

  1. School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, 102249, China

    Jinjiang Wang, Laibin Zhang & Lixiang Duan

  2. Department of Mechanical Engineering, University of Connecticut, Mansfield, CT, 06269, USA

    Robert X. Gao

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  1. Jinjiang Wang
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  2. Laibin Zhang
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Correspondence to Jinjiang Wang.

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Wang, J., Zhang, L., Duan, L. et al. A new paradigm of cloud-based predictive maintenance for intelligent manufacturing. J Intell Manuf 28, 1125–1137 (2017). https://doi.org/10.1007/s10845-015-1066-0

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