Industrial Fault Detection Using Spatio-Temporal Variational Graph Attention Autoencoder
Keywords:
Industrial fault detection, Multivariate time series detection, EncoderAbstract
In critical domains such as industrial IoT, data-center operations, and financial risk control, accurate anomaly detection on multivariate time series (MTS) is essential for ensuring system reliability and security. Anomalies in MTS are rarely isolated numerical deviations; instead, they manifest as subtle disruptions of the normal cooperative patterns among interrelated variables. Specifically, while the encoder can perceive graph structure, the decoder is often reduced to a structure-agnostic multilayer perceptron (MLP), causing valuable structural information to be lost during reconstruction and thus limiting the precision of “relationship anomaly” detection.
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Copyright (c) 2025 Shuyuan Xu
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