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To get a fault diagnosis system with long-term stable performance, an intelligent full-stage stable fault diagnosis method for subsea production system is proposed. A data-driven fault diagnosis model and a model-based fault diagnosis model are simultaneously established. Among them, the model with better performance is used in the current stage of fault diagnosis. The digital twin model is used to provide virtual data to data-driven models, which shortens the training cycle of the model. The data from the South China Sea is used to study the performance of the model. The results showed that the model-based diagnostic model has better performance when there is insufficient training data, and better performance when there is sufficient data. The results also show the long-term stable diagnostic performance of the proposed method.

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Intelligent Fault Diagnosis for Subsea Production System

  • 章节

  • 发表日期：

    2025年

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  • Baoping Cai,
  • Yiliu Liu,
  • Yonghong Liu,
  • Yixin Zhao,
  • Xiaoyan Shao

摘要

To get a fault diagnosis system with long-term stable performance, an intelligent full-stage stable fault diagnosis method for subsea production system is proposed. A data-driven fault diagnosis model and a model-based fault diagnosis model are simultaneously established. Among them, the model with better performance is used in the current stage of fault diagnosis. The digital twin model is used to provide virtual data to data-driven models, which shortens the training cycle of the model. The data from the South China Sea is used to study the performance of the model. The results showed that the model-based diagnostic model has better performance when there is insufficient training data, and better performance when there is sufficient data. The results also show the long-term stable diagnostic performance of the proposed method.