【摘要】 大容量超临界和超超临界循环流化床锅炉是当今循环流化床燃烧技术发展的主流，但大容量循环流化床锅炉耐火材料炉墙缺少快速精准的裂纹测量方法与自动化和智能化检测手段。针对这一问题，利用卷积神经网络和支持向量机对有裂纹和无裂纹壁面图像进行分类，实现裂纹智能识别，同时建立裂纹软测量模型，验证数字图像处理技术测量裂纹宽度和长度的精度和可靠性，又通过循环流化床锅炉内炉墙裂纹图像进行实例分析验证。结果表明，在图像数据量较大的条件下，卷积神经网络的分类性能优于支持向量机，准确率达到90%以上；数字图像处理技术对于不同宽度和形状裂纹的测量精度不同，裂纹宽度约2 mm时，数字图像处理技术精度最高，数字图像处理技术能够有效地识别和测量裂纹宽度。实际裂纹宽度和长度偏差较小，数字图像处理技术能够适应实际环境中的使用需求。更多还原

【Abstract】 Large-capacity supercritical and ultra-supercritical circulating fluidized bed boilers are the mainstream of today’s circulating fluidized bed combustion technology development, but the refractory wall of large-capacity circulating fluidized bed boilers lacks a fast and accurate crack measurement method with automated and intelligent inspection means. To address this problem, convolutional neural network and support vector machine are used to classify the images of cracked and uncracked wall to realize the intelligent identification of cracks,and at the same time, a soft measurement model of cracks is established to validate the accuracy and reliability of the digital image processing technology to measure the width and length of cracks, and then an example analysis is carried out to validate the images of cracks of the furnace wall inside the circulating fluidized bed boiler. The results show that under the condition of large amount of image data, the classification performance of convolutional neural network is better than that of support vector machine, and the accuracy rate reaches more than 90%; the measurement accuracy of digital image processing technology for cracks of different widths and shapes is different, and the digital image processing technology has the highest accuracy when the width of the crack is about 2 mm, and the digital image processing technology can recognize and measure the width of the crack efficiently. The actual crack width and length deviation is small, and the digital image processing technology can be adapted to the needs of use in the actual environment.更多还原