Review on Graph Neural Networks for Process Soft Sensor Development, Fault Diagnosis, and Process Monitoring

The advances of data-driven modeling methods bring new opportunities to numerous intractable tasks in industrial process modeling and exploration. Nevertheless, the extension of these applications has encountered challenges: reliance on large amounts of high-quality training data, generating physically inconsistent solutions, and low interpretability. There is a growing consensus that graph neural networks (GNNs) offer a promising solution for the above challenges by integrating variable interactions, process mechanisms, and expert knowledge into data-driven modeling methods. This review introduces a range of classic GNN architectures and highlights how they address challenges in traditional process modeling,…

• NN
  National Natural Science Foundation of China

  Awards: U23A20328, 62022073
• NS
  National Science and Technology Council

  Award: NSTC 113-2221-E-007-012-MY2