Chinese Journal of Ship Research, Volume. 17, Issue 5, 289(2022)

Fault diagnosis of marine diesel engines based on graph convolutional network under unbalanced datasets

Ruihan WANG1,2, Hui CHEN1,2, Cong GUAN1,2, and Mengzhuo HUANG1,2
Author Affiliations
  • 1Key Laboratory of High Performance Ship Technology of Ministry of Education,Wuhan University of Technology, Wuhan 430063, China
  • 2School of Naval Architecture,Ocean and Engery Power Engineering, Wuhan University of Technology, Wuhan 430063, China
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    References(12)

    [22] [22] KHASGANI H, HASANZADEH A, FARAHAT A, et al. Fault detection isolation in industrial wks using graph convolutional neural wks[C]Proceedings of 2019 IEEE International Conference on Prognostics Health Management. San Francisco: IEEE, 2019: 1–7.

    [23] [23] KIPF T N, WELLING M. Semisupervised classification with graph convolutional wks[C]Proceedings of the 5th International Conference on Learning Representations. Toulon: OpenReview. , 2017.

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    Ruihan WANG, Hui CHEN, Cong GUAN, Mengzhuo HUANG. Fault diagnosis of marine diesel engines based on graph convolutional network under unbalanced datasets[J]. Chinese Journal of Ship Research, 2022, 17(5): 289

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    Paper Information

    Category: Marine Machinery, Electrical Equipment and Automation

    Received: Apr. 17, 2022

    Accepted: --

    Published Online: Mar. 26, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.02859

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