Chemical processes usually exhibit complex, high-dimensional, time-varying, and non-Gaussian characteristics, and the diagnosis of faults in chemical processes is particularly important. However, many current fault diagnosis methods do not consider the temporal correlation of process data, feature selection, and feature sequence arrangement. To solve this problem, this paper presents a fault diagnosis method using a dynamic convolutional neural network, based on a genetic algorithm (GA), for optimizing a feature sequence. First, the input data are transformed into a two-dimensional matrix by adding the dimension of time characteristics. Second, the GA is used to select the features, and the sequence of the selected features is optimized. Finally, the optimized feature sequence is input into the convolutional neural network (CNN) to obtain the final diagnosis results. The Tennessee Eastman chemical process is used for experimental analysis, and the proposed model is compared with the weighted cascade forest, deep belief network (DBN), optimized DBN, long short-term memory + CNN and feature selection using random forest models. The experimental results show that the proposed model has higher diagnostic accuracy. The average diagnosis rate of 20 faults is found to be 89.72%.

化学过程通常表现出复杂的、高维的、时变的、非高斯特征，化学过程中故障的诊断尤为重要。然而，目前许多故障诊断方法没有考虑过程数据、特征选择和特征序列排列的时间相关性。为了解决这个问题，本文提出了一种基于遗传算法（GA）的动态卷积神经网络的故障诊断方法，用于优化特征序列。首先，通过添加时间特征的维度，将输入数据转化为二维矩阵。其次，利用遗传算法进行特征选择，对选择特征的序列进行优化。最后，将优化后的特征序列输入卷积神经网络（CNN），得到最终的诊断结果。采用田纳西伊士曼化学过程进行实验分析，将提出的模型与加权级联森林、深度信念网络（DBN）、优化的DBN、长短期记忆+CNN和使用随机森林模型的特征选择进行比较。实验结果表明，所提出的模型具有较高的诊断准确率。20个故障的平均诊断率为89.72%。实验结果表明，该模型具有较高的诊断准确率。20个故障的平均诊断率为89.72%。实验结果表明，该模型具有较高的诊断准确率。20个故障的平均诊断率为89.72%。