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Predictive Maintenance Based on Identity Resolution and Transformers in IIoT

Author

Listed:
  • Zhibo Qi

    (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
    China Academy of Information and Communications Technology, Beijing 100191, China
    These authors contributed equally to this work.)

  • Lei Du

    (School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
    These authors contributed equally to this work.)

  • Ru Huo

    (School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
    Future Network Research Center, Purple Mountain Laboratories, Nanjing 211111, China
    These authors contributed equally to this work.)

  • Tao Huang

    (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
    Future Network Research Center, Purple Mountain Laboratories, Nanjing 211111, China
    These authors contributed equally to this work.)

Abstract

The burgeoning development of next-generation technologies, especially the Industrial Internet of Things (IIoT), has heightened interest in predictive maintenance (PdM). Accurate failure forecasting and prompt responses to downtime are essential for improving the industrial efficiency. Traditional PdM methods often suffer from high false alarm rates and inefficiencies in complex environments. This paper introduces a predictive maintenance framework using identity resolution and a transformer model. Devices receive unique IDs via distributed identifiers (DIDs), followed by a state awareness model to assess device health from sensor signals. A sequence prediction model forecasts future signal sequences, which are then used with the state awareness model to determine future health statuses. Combining these predictions with unique IDs allows for the rapid identification of facilities needing maintenance. Experimental results show superior performance, with 99% accuracy for the state awareness model and a mean absolute error (MAE) of 0.062 for the sequence prediction model, underscoring the effectiveness of the framework.

Suggested Citation

  • Zhibo Qi & Lei Du & Ru Huo & Tao Huang, 2024. "Predictive Maintenance Based on Identity Resolution and Transformers in IIoT," Future Internet, MDPI, vol. 16(9), pages 1-18, August.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:9:p:310-:d:1465341
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