DAMPNN: Dynamic Adaptive Message Passing Neural Network for Industrial Soft Sensor

Data-driven soft sensor modeling has received much attention in industrial processes. Most of the existing soft sensor approaches have not considered the complex dynamic spatial coupling characteristics between process variables. Recently, graph-based soft sensor modeling methods have started to show powerful expressive ability in capturing relational dependencies. However, existing graph-based soft sensor models still confront several limitations: 1) these models usually depend on predefined graph structures or local dynamic graph; 2) they fail to study dynamic message passing mechanism; 3) they have not considered the importance of extracted features from the entire graph. To handle these problems, in this study, we develop a dynamic adaptive message passing neural network (DAMPNN) for industrial soft sensor. The main novelty lies in an integration of our designed three modules into DAMPNN. First, we propose an adaptive graph learning module to automatically capture mutual relationships between process variables instead of a predefined adjacency matrix. Then, we design a dynamic message passing module to aggregate neighborhood information and update graph representation. In addition, a dual self-attention module is embedded into the top layer to concurrently emphasize informative features and time points for fine-grained soft sensor modeling. Finally, comprehensive comparison results on two real-world industrial cases demonstrate that DAMPNN outperforms the existing graph-based soft sensor methods.