Novel Soft Sensor Model based on Spatio-Temporal Attention

Industrial process data is usually time series data collected by sensors with high non-linearity, dynamics and high noise. Many existing soft sensor models usually focus on the temporal features of industrial process data, while ignoring the spatial interaction between auxiliary variables. Therefore, a novel spatio-temporal attention neural network (STAN) is proposed for dynamic soft sensor modeling of industrial processes. The temporal attention module and the spatial attention module extract the temporal and spatial interaction features of sequence data, respectively. Then the spatio-temporal fusion module adaptively controls the fusion of the temporal and spatial interactive features to obtain the spatio-temporal interactive features. Finally, the spatio-temporal interaction features are input into the fully connected layer and the highway layer of the STAN to output the final prediction result. The STAN is applied in the dynamic soft sensor modeling of polypropylene melt index. Compared with backpropagation neural network (BPNN), extreme learning machine (ELM), long short-term memory (LSTM) and convolutional LSTM ( $CNN+LSTM$ ), the STAN achieves the state-of-the-art results.