AI RESEARCH
Graph Autoencoder for Process Monitoring
arXiv CS.LG
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ArXi:2602.03004v2 Announce Type: replace To improve the reliability and interpretability of industrial process monitoring, this article proposes a Causal Graph Spatial-Temporal Autoencoder (CGSTAE). The network architecture of CGSTAE combines two components: a correlation graph structure learning module based on spatial self-attention mechanism (SSAM) and a spatial-temporal encoder-decoder module utilizing graph convolutional long-short term memory