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Knowledge Graph Construction Based on a Joint Model for Equipment Maintenance

Author

Listed:
  • Ping Lou

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Dan Yu

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Xuemei Jiang

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Jiwei Hu

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Yuhang Zeng

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Chuannian Fan

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

Abstract

Under the background of intelligent manufacturing, industrial systems are developing in a more complex and intelligent direction. Equipment maintenance management is facing significant challenges in terms of maintenance workload, system reliability and stability requirements and the overall skill requirements of maintenance personnel. Equipment maintenance management is also developing in the direction of intellectualization. It is important to have a method to construct a domain knowledge graph and to organize and utilize it. As is well known, traditional equipment maintenance is mainly dependent on technicians, and they are required to be very familiar with the maintenance manuals. But it is very difficult to manage and exploit a large quantity of knowledge for technicians in a short time. Hence a method to construct a knowledge graph (KG) for equipment maintenance is proposed to extract knowledge from manuals, and an effective maintenance scheme is obtained with this knowledge graph. Firstly, a joint model based on an enhanced BERT-Bi-LSTM-CRF is put forward to extract knowledge automatically, and a Cosine and Inverse Document Frequency (IDF) based on semantic similarity a presented to eliminate redundancy in the process of the knowledge fusion. Finally, a Decision Support System (DSS) for equipment maintenance is developed and implemented, in which knowledge can be extracted automatically and provide an equipment maintenance scheme according to the requirements. The experimental results show that the joint model used in this paper performs well on Chinese text related to equipment maintenance, with an F1 score of 0.847. The quality of the knowledge graph constructed after eliminating redundancy is also significantly improved.

Suggested Citation

  • Ping Lou & Dan Yu & Xuemei Jiang & Jiwei Hu & Yuhang Zeng & Chuannian Fan, 2023. "Knowledge Graph Construction Based on a Joint Model for Equipment Maintenance," Mathematics, MDPI, vol. 11(17), pages 1-23, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3748-:d:1229899
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    References listed on IDEAS

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    1. Jonathan P. Tennant & Harry Crane & Tom Crick & Jacinto Davila & Asura Enkhbayar & Johanna Havemann & Bianca Kramer & Ryan Martin & Paola Masuzzo & Andy Nobes & Curt Rice & Bárbara Rivera-López & Tony, 2019. "Ten Hot Topics around Scholarly Publishing," Publications, MDPI, vol. 7(2), pages 1-24, May.
    2. Peijie Jiang & Xiaomeng Ruan & Zirong Feng & Yanyun Jiang & Bin Xiong, 2023. "Research on Online Collaborative Problem-Solving in the Last 10 Years: Current Status, Hotspots, and Outlook—A Knowledge Graph Analysis Based on CiteSpace," Mathematics, MDPI, vol. 11(10), pages 1-20, May.
    3. Yong Chen & Xinkai Ge & Shengli Yang & Linmei Hu & Jie Li & Jinwen Zhang, 2023. "A Survey on Multimodal Knowledge Graphs: Construction, Completion and Applications," Mathematics, MDPI, vol. 11(8), pages 1-27, April.
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    Cited by:

    1. Yan Wang & Yuepan Liu & Kai Ding & Shirui Wei & Xuhui Zhang & Youjun Zhao, 2023. "Dynamic Optimization Method of Knowledge Graph Entity Relations for Smart Maintenance of Cantilever Roadheaders," Mathematics, MDPI, vol. 11(23), pages 1-23, November.

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