Knowledge-Based Temporal GCN: A Spatial–Temporal Fault Diagnosis Method for Blast Furnace Ironmaking Process With Imbalanced Data

Fault diagnosis is essential to the safe operation of the blast furnace ironmaking process (BFIP). Currently, BFIP fault diagnosis still faces the following challenges: difficult-to-analyze spatial-temporal dependencies, difficult-to-use on-site knowledge, and imbalanced fault samples. In this case, a knowledge-based temporal graph convolution network (KB-TGCN) is proposed to address the above problems. First, the knowledge-based graph structure is built on the spatial and variable levels by utilizing the locations of sensors and the calculation relationships between variables. Subsequently, 1-D temporal information extractors (TIEs) are embedded in the nodes of GCN, and the TIEs can capture the mutilscale temporal information while maintaining the original spatial relationships. Therefore, KB-TGCN can fully explore the temporal and spatial information of BF. Additionally, to overcome the issue of data imbalance, the model is trained end to end by a focal loss (FL) function with an data-specific balance factor. Finally, the method is verified on the BF dataset, and the classification accuracy is higher than the baseline methods and the general spatial-temporal diagnosis method.