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<p>Offshore platforms are large, complex structures designed for long-term service, and they are characterized by high risk and significant investment. Ensuring the safety and reliability of in-service offshore platforms requires intelligent operation and maintenance strategies. Digital twin technology can enable the accurate description and prediction of changes in the platform’s physical state through real-time monitoring data. This technology is expected to revolutionize the maintenance of existing offshore platform structures. A digital twin system is proposed for real-time assessment of structural health, prediction of residual life, formulation of maintenance plans, and extension of service life through predictive maintenance. The system integrates physical entities, digital models, intelligent predictive maintenance tools, a visualization platform, and interconnected modules to provide a comprehensive and efficient maintenance framework. This paper examines the current development status of core technologies in physical entity monitoring, digital model construction, and intelligent predictive maintenance. It also outlines future directions for the advancement of these technologies within the digital twin system, offering technical insights and practical references to support further research and applications of digital twin technology in offshore platform structures.</p>

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Research Progress in Predictive Maintenance of Offshore Platform Structures Based on Digital Twin Technology

  • 期刊论文

  • 发表日期：

    2025年2月24日

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  • Jincheng Sha,
  • Jiancheng Leng,
  • Houbin Mao,
  • Jinyuan Pei,
  • Kaixin Diao

摘要

Offshore platforms are large, complex structures designed for long-term service, and they are characterized by high risk and significant investment. Ensuring the safety and reliability of in-service offshore platforms requires intelligent operation and maintenance strategies. Digital twin technology can enable the accurate description and prediction of changes in the platform’s physical state through real-time monitoring data. This technology is expected to revolutionize the maintenance of existing offshore platform structures. A digital twin system is proposed for real-time assessment of structural health, prediction of residual life, formulation of maintenance plans, and extension of service life through predictive maintenance. The system integrates physical entities, digital models, intelligent predictive maintenance tools, a visualization platform, and interconnected modules to provide a comprehensive and efficient maintenance framework. This paper examines the current development status of core technologies in physical entity monitoring, digital model construction, and intelligent predictive maintenance. It also outlines future directions for the advancement of these technologies within the digital twin system, offering technical insights and practical references to support further research and applications of digital twin technology in offshore platform structures.