Pipe wall-thinning is a phenomenon whereby the thickness of pipes in a nuclear power plant decreases over time owing to extended operational years. This thickness reduction is caused by various long-term thermal aging mechanisms such as Flow-Accelerated Corrosion, Liquid Droplet Impingement Erosion, cavitation, and flashing. Reducing the thickness of the secondary system pipes to the point of rupture can lead to severe human casualties and significant economic losses. Consequently, domestic power plant operators regularly manage the power plant pipes. Ongoing research focuses on the identification and management of pipe wall-thinning. However, previous studies have encountered problems in accurately judging the decrease in pipe wall-thinning in the presence of noise. To overcome this, Convolutional Neural Network (CNN) models based on image feature analysis have been used. This approach allows for the differentiation of pipe wall-thinning feature from small-sized noise in a single image. However, there were difficulties in making accurate judgments for large-sized noise that resembled the feature of pipe wall-thinning. This paper aims to analyze the limitations of the current pipe wall-thinning evaluation methods and to achieve accurate pipe wall-thinning discrimination through time-series analysis of continuous pipe wall-thinning data. The proposed method employs a Convolutional Recurrent Neural Network (CRNN) model, integrating the Recurrent Neural Network(RNN) model with the CNN model. The image feature of the pipes, extracted using CNN, are utilized as inputs for the RNN. This enables the observation of how the image features of the pipes change over time. This feature differentiates from the time-series feature of noise that occurs suddenly. Through this method, the paper proposes a new approach for effectively identifying the gradual decrease in pipe wall-thinning, enabling precise assessment of pipe wall-thinning progression.

中文翻译：

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采用卷积递归神经网络模型的抗噪声管道减壁判别系统

管道壁厚减薄是核电站中由于运行年限延长而导致管道厚度逐渐减小的现象。这种厚度减少是由各种长期热老化机制引起的，例如流动加速腐蚀、液滴冲击侵蚀、空化和闪蒸。将二次系统管道的厚度减小到破裂点可能会导致严重的人员伤亡和重大的经济损失。因此，国内电厂运营商定期对电厂管道进行管理。正在进行的研究重点是管道壁减薄的识别和管理。然而，先前的研究在准确判断存在噪声的情况下管道壁减薄的减少方面遇到了问题。为了克服这个问题，人们使用了基于图像特征分析的卷积神经网络（CNN）模型。这种方法可以将管壁减薄特征与单个图像中的小尺寸噪声区分开来。但对于类似管壁减薄特征的大尺寸噪声，很难做出准确判断。本文旨在分析现有管道减壁评价方法的局限性，通过对连续管道减壁数据的时间序列分析，实现管道的准确减壁判别。该方法采用卷积递归神经网络（CRNN）模型，将递归神经网络（RNN）模型与CNN模型相结合。使用 CNN 提取的管道图像特征被用作 RNN 的输入。这使得能够观察管道的图像特征如何随时间变化。该特征与突然发生的噪声的时间序列特征不同。通过该方法，本文提出了一种有效识别管道壁厚逐渐减少的新方法，从而能够精确评估管道壁厚减薄的进展情况。