Research on detection technology for thermal leakage and wall thickness reduction in steam pipelines
基于红外热成像的非接触式蒸汽管道缺陷智能检测与评估方法研究

摘要

针对蒸汽管道热泄漏与壁厚减薄的安全检测难题，研究提出基于红外热成像的非接触式智能检测方法。通过融合多源数据与先进的机器学习算法，构建热泄漏强度、温度场分布与壁厚减薄之间的关联模型并采用自适应算法补偿环境干扰，显著提高检测鲁棒性。创新建立包含热图特征、温度梯度及材质衰减的综合评估体系，实现对管道缺陷的精准识别与量化分析方法。实验表明，该方法在电站核心管路的早期缺陷预警中表现出色，检测效率较传统方法提升40%以上，成本降低约35%。研究成果不仅为蒸汽管道安全检测提供新型技术解决方案，对推动工业无损检测向智能化方向发展，具有显著的工程应用价值与学术拓展空间。

Abstract

To address the safety inspection challenges of steam pipeline thermal leakage and wall thickness reduction, this study proposes a non-contact intelligent detection method based on infrared thermography. By integrating multi-source data with advanced machine learning algorithms, an association model linking thermal leakage intensity, temperature field distribution, and wall thickness reduction is constructed. Additionally, an adaptive algorithm is employed to compensate for environmental interference, significantly enhancing detection robustness. An innovative comprehensive evaluation system incorporating thermal pattern features, temperature gradients, and material attenuation is established to achieve precise identification and quantitative analysis of pipeline defects. Experiments demonstrate that this method performs exceptionally in early defect warning for core pipelines in power stations, with detection efficiency improved by over 40% and costs reduced by approximately 35% compared to traditional methods. The research findings not only provide novel technical solutions for steam pipeline safety inspection but also drive the development of industrial non-destructive testing towards intelligence, possessing significant engineering application value and academic expansion potential. igence, offering significant engineering application value and academic expansion potential.