CGSTAE learns correlation graphs with spatial self-attention, derives causal graphs via a three-step invariance algorithm, and uses GCLSTM encoder-decoder to monitor industrial processes on Tennessee Eastman and air separation data.
Graph convolutional network- based method for fault diagnosis using a hybrid of measurement and prior knowledge,
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Graph Autoencoder for Process Monitoring
CGSTAE learns correlation graphs with spatial self-attention, derives causal graphs via a three-step invariance algorithm, and uses GCLSTM encoder-decoder to monitor industrial processes on Tennessee Eastman and air separation data.