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 Τ2 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. © 2021, Zhejiang University Press. All right reserved.