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A collaborative model for predictive maintenance of after-sales equipment based on digital twin

Author

Listed:
  • Xiao Li
  • Hongfei Liang
  • Yuchen Chen
  • Yuanpeng Ruan
  • Lei Wang

Abstract

In response to the demands of users for prompting fault diagnosis and maintenance, equipment manufacturers require more advanced maintenance technologies for real-time monitoring, prediction, and remote guidance. Based on digital twin, this paper puts forward a seven-dimensional model of collaborative maintenance and a collaborative model for after sales maintenance service, which enables manufacturers to provide more effective and timely service and support to their customers. Taking a bottled water capping process as an example, it constructs a digital twin-driven model for predicting the remaining effective life of devices, a digital twin service platform with a maintenance knowledge database. Based on the forward variable combining the current state and state duration from hidden semi-Markov chain, and the improved formula for calculating the remaining effective life of equipment state, the feasibility of the proposed seven-dimensional collaborative maintenance model and the collaborative model for after sales maintenance service are verified. [Submitted: 20 July 2021; Accepted: 8 August 2022]

Suggested Citation

  • Xiao Li & Hongfei Liang & Yuchen Chen & Yuanpeng Ruan & Lei Wang, 2023. "A collaborative model for predictive maintenance of after-sales equipment based on digital twin," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 17(5), pages 795-831.
    • Handle: RePEc:ids:eujine:v:17:y:2023:i:5:p:795-831
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