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April 1, 2026工程施工新技术0 citationsOpen Access

人工智能在油气管道缺陷识别与定量评估中的应用

李李蒙奇

Key Points

  • The research aims to enhance the accuracy of defect identification and quantitative assessment in oil and gas pipelines using artificial intelligence.
  • Conducted a systematic review of AI technologies in defect identification and assessment
  • Explored key technical pathways including magnetic leakage data processing and image recognition
  • Analyzed typical algorithms' effectiveness and limitations through engineering case studies
  • Achieved defect identification accuracy of over 95%
  • Controlled quantitative error within ±5%
  • Provided significant support for the lifecycle management of oil and gas pipelines

Abstract

随着油气管道建设的规模扩大与服役年限增长,管道缺陷的精准识别与定量评估成为保障能源运输安全 的核心挑战。传统检测方法存在效率低、主观性强、量化误差大等局限,而人工智能技术凭借其强大的数据处理与模 式识别能力,为管道缺陷检测提供了智能化解决方案。本文系统梳理了人工智能在油气管道缺陷识别与定量评估中的 技术路径,涵盖漏磁数据处理、图像智能识别、三维建模与预测性维护等关键环节,分析了典型算法的应用效果与局 限性,并结合工程案例验证了ai技术的经济性与可靠性。研究结果表明,人工智能技术可将缺陷识别准确率提升至 95%以上,量化误差控制在±5%以内,为油气管道全生命周期管理提供了重要支撑。

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Cite This Study

李蒙奇 (2026) studied this question.

synapsesocial.com/papers/69cd79e15652765b073a6ba9https://doi.org/10.37155/2811-0609-0502-31
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