针对高压管汇损伤需要提高检测效率和准确率的问题，提出一种基于多尺度一维卷积神经网络（multi-scale one-dimensional convolutional neural network, MS-1DCNN）的弯管冲蚀损伤智能检测新方法，即用多尺度卷积层代替传统的单一尺度卷积层。在MS-1DCNN模型中，把通过模拟实验所得弯管冲蚀损伤原始时域信号作为多尺度一维卷积神经网络的输入，这样能解决传统方法依赖人工提取特征和专家知识的问题；然后，通过多尺度卷积层和池化层的交替连接对输入信号进行特征提取；最后，经由输出层输出弯管冲蚀损伤分类结果。模型试验结果表明：基于MS-1DCNN弯管冲蚀损伤检测方法可以有效检测出弯管冲蚀损伤，且平均检测准确率达到99.18%。研究可为高压管汇冲蚀损伤智能检测提供一种新思路。

In order to improve the detection efficiency and accuracy of high pressure manifold damage, a new intelligent detection method for bending erosion damage based on a multi-scale one-dimensional convolutional neural network (MS-1DCNN) was proposed, and a multi-scale convolution layer was used to replace the traditional single-scale convolution layer. In the MS-1DCNN model, the original time domain signal of bending erosion damage obtained through simulation experiments was used as the input of multi-scale one-dimensional convolutional neural networks, which can solve the problem that traditional methods rely on manual feature extraction and expert knowledge. Then, the feature extraction of input signals was carried out by alternating connection of multi-scale convolution layer and pooling layer. Finally, the classification results of erosion damage of bent pipe were output through the output layer. The model test results show that the erosion damage detection method based on MS-1DCNN can effectively detect erosion damage of bent pipes, and the average detection accuracy is 99.18%. Research can provide a new approach for quantitative intelligent detection of erosion damage in high-pressure manifolds.