Optical Communication Technology, Volume. 49, Issue 3, 27(2025)

Fault identification algorithm for OLT equipment based on deep cross network and multi-task learning

MAO Shilong1, ZHAO Zanshan1,2,3, WANG Haoyu1, and GAO Guanjun1
Author Affiliations
  • 1School of Electronic, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 2Hainan Acoustics Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Haikou 570105, China
  • 3Lingshui Marine Information Hainan Field Scientific Observation and Research Station, Lingshui Hainan 572423, China
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    References(9)

    [1] [1] NYARKO O, ADEKOYA A F, WEYORI B A. Predicting the actual location of faults in underground optical networks using linear regression[J]. Engineering Reports, 2021, 3(3): 1-13.

    [2] [2] LUN H, LIU X, CAI M, et al. GAN based soft failure detection and identification for long-haul coherent transmission systems[C]//2021 Optical Fiber Communications Conference and Exhibition (OFC), March 6 -11, 2021, San Francisco, USA. Piscataway: IEEE, 2021: 1-3.

    [3] [3] WANG D, ZHANG Z, ZHANG M, et al. The role of digital twin in optical communication: fault management, hardware configuration, and transmission simulation [J]. IEEE Communications Magazine, 2021, 59 (1): 133-139.

    [5] [5] LIU S L, WANG D, ZHANG C, et al. Semi-supervised anomaly detection with imbalanced data for failure detection in optical networks [C]//Optical Fiber Communication Conference (OFC), June 6-10, 2021, San Francisco, USA. Washington: Optica Publishing Group, 2021: Th1A-24-1-Th1A-24-3.

    [7] [7] ZHANG C, SUN Z, YANG W, et al. Expertise-enhanced machine learning for failure detection on field-deployed optical modules[J]. Journal of Lightwave Technology, 2025, 43(1): 137-154.

    [8] [8] ZHANG C, WANG D, WANG L, et al. Cause-aware failure detection using an interpretable XGBoost for optical networks [J]. Optics Express, 2021, 29(20): 31974-31992.

    [9] [9] ZHANG C, WANG D, JIA J, et al. Potential failure cause identification for optical networks using deep learning with an attention mechanism [J]. Journal of Optical Communications and Networking, 2022, 14 (2): A122-A133.

    [10] [10] HAHN G J, SHAPIRO S S. Statistical models in engineering: chapter 3: data standardization and transformation [M]. New York: John Wiley & Sons, Inc., 1994: 45-66.

    [11] [11] WANG R, SHIVANNA R, CHENG D, et al. DCN V2: improved deep & cross network and practical lessons for web-scale learning to rank systems[C]//The Web Conference 2021(WWW '21), April 19-23, 2021, Ljubl jana , Slovenia. New York: Association for Computing Machinery, 2021: 1785-1797.

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    MAO Shilong, ZHAO Zanshan, WANG Haoyu, GAO Guanjun. Fault identification algorithm for OLT equipment based on deep cross network and multi-task learning[J]. Optical Communication Technology, 2025, 49(3): 27

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    Paper Information

    Special Issue:

    Received: Mar. 17, 2025

    Accepted: Jun. 27, 2025

    Published Online: Jun. 27, 2025

    The Author Email:

    DOI:10.13921/j.cnki.issn1002-5561.2025.03.005

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