A Spatiotemporal Industrial Soft Sensor Modeling Scheme for Quality Prediction with Missing Data

Quality prediction is important for precise control of industrial processes and improvements of product quality. The nonlinear and dynamic features of time series data can be effectively extracted by the appropriate soft sensor models for quality prediction. However, the temporal features may be often considered by the conventional methods, ignoring the important spatial features, which makes the soft sensor models not have stronger generalization abilities because of insufficient feature information for quality prediction. Moreover, due to the unstable transmission signals, equipment failures, and sensor packet losses, missing values maybe present in the industrial data, which influences the accuracy of soft sensor models. To overcome those problems, a novel spatiotemporal industrial soft sensor modeling scheme is designed for quality prediction. To be specific, the generate adversarial imputation network is firstly applied for data imputation. Then, the k -nearest neighbor mutual information is used for constructing the adjacency matrix of graph attention network with the purpose of adaptive measuring the spatial correlations among process variables at different moments. After that, the temporal attention mechanism is introduced for enhancing the temporal feature extraction capability of minimal gated unit, aiming at improving the quality prediction performance. Finally, sufficient simulation experiments are conducted by a typical industrial process, hot rolling process, to demonstrate the superiority of the proposed scheme compared with some classical and advanced algorithms.