SENGraph: A Self-Learning Evolutionary and Node-Aware Graph Network for Soft Sensing in Industrial Processes

The last decade has witnessed the growing prevalence of deep models on soft sensing in industrial processes. However, most of the existing soft sensing models are developed to learn from regular data in the Euclidean space, ignoring the complex coupling relations among process variables. On the other hand, graph networks are gaining attraction in handling non-Euclidean relations in industrial data. However, the existing graph networks on soft sensing models still suffer from two major issues: 1) how to capture the intervariable structural relations and intravariable temporal dependencies from dynamic and strongly coupled industrial data and 2) how to learn from nodes with distinctive importance for the soft sensing task. To address these problems, we propose a self-learning evolutionary and node-aware graph network (SENGraph) for industrial soft sensing. We first develop a self-learning graph generation (SLG) module to combine the coarse-and fine-grained graphs to capture the global trend and local dynamics from process data. Then, we build a self-evolutionary graph module (EGM) to obtain diversified node features from the entire graph using mutation and crossover strategies. Finally, we design a node-aware module (NAM) to highlight the informative nodes and suppress the less significant ones to further improve the discriminative ability of the downstream soft sensing. Extensive experimental results and analysis on four real-world industrial datasets demonstrate that our proposed SENGraph model outperforms the existing state-of-the-art (SOTA) soft sensing methods.