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Digital-Triplet: a new three entities digital-twin paradigm for equipment fault diagnosis

Author

Listed:
  • Huang Zhang

    (Zhejiang University)

  • Zili Wang

    (Zhejiang University)

  • Shuyou Zhang

    (Zhejiang University)

  • Lemiao Qiu

    (Zhejiang University)

  • Yang Wang

    (Zhejiang University)

  • Feifan Xiang

    (Zhejiang University)

  • Zhiwei Pan

    (Zhejiang University)

  • Linhao Zhu

    (Canny Elevator Co., Ltd.)

  • Jianrong Tan

    (Zhejiang University)

Abstract

Current equipment fault diagnosis faces challenges due to the difficulties in arranging sensors to collect effective data and obtaining diverse fault data for studying fault mechanisms. The lack of data results in disconnection between data from different spaces, posing a challenge to forming a closed loop of data and hindering the development of digital twin (DT) driven fault diagnosis (FD). To address these issues, a new DT paradigm Digital-Triplet is proposed. This paradigm comprises three entities: a physical entity, a semi-physical entity, and a virtual entity. A semi-physical entity is created by implementing the "six-D" process on the physical entity. A new six dimensional structure is formed through the addition of the semi-physical entity. The new structure streamlines the construction of fault datasets, enhances sensor data acquisition, and tightly links different data spaces, thereby promoting the application of DT in equipment FD. Subsequently, the elevator is selected as a case study to illustrate the Digital-Triplet framework in detail. The results demonstrate that the Digital-Triplet framework can effectively expand the fault dataset and improve data collection efficiency through optimized sensor placement, thereby promoting fault diagnosis.

Suggested Citation

  • Huang Zhang & Zili Wang & Shuyou Zhang & Lemiao Qiu & Yang Wang & Feifan Xiang & Zhiwei Pan & Linhao Zhu & Jianrong Tan, 2025. "Digital-Triplet: a new three entities digital-twin paradigm for equipment fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 36(7), pages 4895-4914, October.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02471-7
    DOI: 10.1007/s10845-024-02471-7
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    References listed on IDEAS

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    1. Muhammad Hassan & Marcus Svadling & Niclas Björsell, 2024. "Experience from implementing digital twins for maintenance in industrial processes," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 875-884, February.
    2. Ainhoa Pujana & Miguel Esteras & Eugenio Perea & Erik Maqueda & Philippe Calvez, 2023. "Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation," Energies, MDPI, vol. 16(2), pages 1-20, January.
    3. Huang, Yufeng & Tao, Jun & Sun, Gang & Wu, Tengyun & Yu, Liling & Zhao, Xinbin, 2023. "A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis," Energy, Elsevier, vol. 270(C).
    4. Tobias Biegel & Patrick Helm & Nicolas Jourdan & Joachim Metternich, 2024. "SSMSPC: self-supervised multivariate statistical in-process control in discrete manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2671-2698, August.
    5. Meyer, Angela, 2021. "Multi-target normal behaviour models for wind farm condition monitoring," Applied Energy, Elsevier, vol. 300(C).
    6. Ali Rohan, 2022. "Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
    7. Xiang, Ling & Yang, Xin & Hu, Aijun & Su, Hao & Wang, Penghe, 2022. "Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks," Applied Energy, Elsevier, vol. 305(C).
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