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Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network

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  • Xia, Liqiao
  • Liang, Yongshi
  • Leng, Jiewu
  • Zheng, Pai

Abstract

Maintenance planning is a significant part of predictive maintenance, which involves task planning, resource scheduling, and prevention. With large-scale sensor systems in modern factories, much data will be captured during monitoring and maintenance of complex industrial equipment. Accumulated data facilitates maintenance planning becomes more thorough and timely. Recently, a knowledge graph (KG) was offered to handle large-scale, unorganized maintenance data semantically, resulting in better data usage. Some prior studies have utilized KG for maintenance planning with semantic searching or graph structure-based algorithms, nevertheless neglecting the prediction of potential linkage. To fill this gap, a maintenance-oriented KG is established firstly based on a well-defined domain-specific ontology schema and accumulated maintenance data. Then, an Attention-Based Compressed Relational Graph Convolutional Network is proposed to predict potential solutions and explain fault in maintenance tasks. Lastly, a maintenance case of oil drilling equipment is carried out, where the proposed model is compared with other cutting-edge models to demonstrate its effectiveness in link prediction. This research is anticipated to shed light on future adoption of KG in maintenance planning recommendations.

Suggested Citation

  • Xia, Liqiao & Liang, Yongshi & Leng, Jiewu & Zheng, Pai, 2023. "Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006834
    DOI: 10.1016/j.ress.2022.109068
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    References listed on IDEAS

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    Cited by:

    1. Lan, Meng & Gardoni, Paolo & Weng, Wenguo & Shen, Kaixin & He, Zhichao & Pan, Rongliang, 2024. "Modeling the evolution of industrial accidents triggered by natural disasters using dynamic graphs: A case study of typhoon-induced domino accidents in storage tank areas," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Liu, Jie & Zheng, Shuwen & Wang, Chong, 2023. "Causal Graph Attention Network with Disentangled Representations for Complex Systems Fault Detection," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Liu, Jintao & Chen, Keyi & Duan, Huayu & Li, Chenling, 2024. "A knowledge graph-based hazard prediction approach for preventing railway operational accidents," Reliability Engineering and System Safety, Elsevier, vol. 247(C).

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