The diagnosis of slug flow is extremely important for the efficient operation of the gas-liquid separator. Traditional fault diagnosis based on the convolutional neural network has not involved the explainability of the convolutional neural network, which makes the model difficult to interpret from the perspective of physical meaning. An explainable diagnostic method based on a fully convolutional neural network is proposed. The class activation mapping, multivariate mutual information, global average pooling and t-distributed stochastic neighbor embedding are combined to analyze the diagnostic process of the network. The experimental results based on simulation data showed that the proposed method can be utilized to interpret the correlation degree between different operating conditions, the importance of each period in the measurement variable, and the engineering significance of the convolutional kernels of the last layer, which provides information supplement for fault reasoning of human experts.

段塞流的诊断对于气液分离器的高效运行极为重要。传统基于卷积神经网络的故障诊断没有涉及卷积神经网络的可解释性，这使得模型难以从物理意义的角度进行解释。提出了一种基于全卷积神经网络的可解释诊断方法。结合类激活映射、多元互信息、全局平均池化和t分布随机邻居嵌入来分析网络的诊断过程。基于仿真数据的实验结果表明，所提出的方法可以用来解释不同工况之间的相关程度，测量变量中每个周期的重要性，