A Graph-Based Time-Frequency Two-Stream Network for Multistep Prediction of Key Performance Indicators in Industrial Processes

Deep learning-based soft sensor modeling methods have been extensively studied and applied to industrial processes in the last decade. However, existing soft sensor models mainly focus on the current step prediction in real time and ignore the multistep prediction in advance. In actual industrial applications, compared to the current step prediction, it is more useful for on-site workers to predict some key performance indicators in advance. Nowadays, multistep prediction task still suffers from two key issues: 1) complex coupling relationships between process variables and 2) long-term dependency learning. To ravel out these two problems, in this article, we propose a graph-based time-frequency two-stream network to achieve multistep prediction. Specifically, a multigraph attention layer is proposed to model the dynamical coupling relationships between process variables from the graph perspective. Then, in the time-frequency two-stream network, multi-GAT is used to extract time-domain features and frequency-domain features for long-term dependency, respectively. Furthermore, we propose a feature fusion module to combine these two kinds of features based on the minimum redundancy and maximum correlation learning paradigm. Finally, extensive experiments on two real-world industrial datasets show that the proposed multistep prediction model outperforms the state-of-the-art models. In particular, compared to the existing SOTA method, the proposed method has achieved 12.40%, 22.49%, and 21.98% improvement in RMSE, MAE, and MAPE on the three-step prediction task using waste incineration dataset.