注意力卷积GRU自编码器及其在工业过程监控的应用

针对现有故障检测算法难以深入并准确地提取数据内在信息的问题，提出注意力卷积门控循环单元自编码器（CGRUA-AE）深度神经网络和基于CGRUA-AE的过程故障检测方法. 采用卷积门控循环单元（ConvGRU）有效地提取输入数据的空间和时间特征；建立基于ConvGRU的自编码器，采用无监督学习对时间序列数据进行特征提取，引入注意力机制对相应的特征进行加权计算，实现对关键特征的有效选择；分别在特征空间与残差空间上建立基于T ²、SPE统计量的过程监控模型，实现对多元数据有效的特征提取和故障检测. 数值案例和田纳西?伊士曼过程故障检测结果表明，CGRUA-AE具有良好的特征提取能力和故障检测能力，性能优于常用的过程故障检测方法.

Attention convolutional GRU-based autoencoder and its application in industrial process monitoring

A new deep neural network with attention convolutional gated recurrent unit-based autoencoder (CGRUA-AE) and a process fault detection method based on CGRUA-AE were proposed aiming at the problem that the existing fault detection algorithms were difficult to extract the internal information of data deeply and accurately. First, a convolutional gated recurrent unit (ConvGRU) was effectively extracted the spatial and temporal features of input data. Secondly, an auto-encoder based on ConvGRU was established, using unsupervised learning to extract features from time series data, introducing an attention mechanism to calculate the weight of corresponding features to realize the effective selection of key features. Finally, the process monitoring model based on $ {T}^{2} $ and SPE statistics were established in feature space and residual space respectively to realizes effective feature extraction and fault detection for multivariate data. Numerical case and Tennessee-Eastman process fault detection results show that CGRUA-AE has good feature extraction ability and fault detection ability, and its performance is superior to the common process fault detection methods.