卷积神经网络在气体泄漏超声识别中的应用

为了克服现有气体泄漏检测方法的不足，提出一种基于卷积神经网络的气体泄漏超声信号识别方法。在设计卷积神经网络网络结构时，通过多次预训练确定网络层数、卷积核数目和尺寸、全连接层神经元数目。同时，选择Inception模块平衡网络宽度和深度，防止过拟合的同时提高网络对尺度的适应性。通过输气管道泄漏实验平台模拟工况中常见的阀门泄漏和垫片泄漏，利用短时傅里叶变换进行时频图表征，在此基础上，建立二分类模型和不同泄漏类型的三分类模型。结果表明，相比二分类模型，不同泄漏类型的三分类模型识别准确率有所降低，添加Inception模块可以有效提高三分类模型的性能。

Application of convolutional neural network in ultrasonic identification of gas leakagel network

In order to overcome the shortcomings of existing gas leakage detection methods, an ultrasonic signal recognition method of gas leakage based on convolutional neural network (CNN) was proposed. When designing the CNN network structure, the number of network layers, the number and size of convolution kernel and the number of fully connected layer neurons were determined by multiple pre-training. Meanwhile, Inception module was selected to balance the width and depth of the network, prevent overfitting and improve the adaptability of the network to scale. The valve leakage and gasket leakage in working conditions were simulated by the gas pipeline leakage experimental platform, and the short-time Fourier transform was used to characterize the time-frequency diagram. Based on this, two-class model and three-class model with different leakage types were established. The results show that compared with two-class model, the recognition accuracy of the three-class model with different leakage types is reduced, and the addition of Inception module can effectively improve the performance of the three-class model.