# Spatio-Temporal Graph Neural Networks for Anomaly Detection in Complex Industrial Processes

## TL;DR

This paper introduces a new graph neural network model for detecting anomalies in industrial processes, which improves accuracy and efficiency compared to existing methods.

## Contribution

The novel ST-VGSAE framework combines spatio-temporal decomposition and attention mechanisms for efficient and robust anomaly detection.

## Key findings

- The proposed model outperforms existing methods in fault detection and false alarm rates.
- The model demonstrates superior noise robustness and real-time performance on the Tennessee Eastman dataset.

## Abstract

With the advancement of intelligent manufacturing strategies, Cyber–Physical Production Systems (CPPSs) generate massive amounts of multidimensional, dynamic, and non-stationary data, posing significant challenges to real-time Process Monitoring. Existing anomaly detection methods often suffer from insufficient feature robustness when dealing with complex spatio-temporal dynamics, high computational complexity, and difficulties in effectively capturing incipient faults within deep topological structures. To address these issues, this paper proposes a Spatio-Temporal Variational Graph Statistical Attention Autoencoder (ST-VGSAE). First, the framework performs end-to-end multi-scale temporal decomposition via an Adaptive Lifting Wavelet Module, which enhances feature robustness while effectively suppressing noise. Furthermore, a spatio-temporal Token statistical self-attention mechanism with linear complexity is incorporated. By modulating local features via global statistics, it significantly reduces computational costs while enhancing anomaly discriminability. Experiments on the Tennessee Eastman (TE) process dataset demonstrate that the proposed model significantly outperforms state-of-the-art methods in key metrics such as the Fault Detection Rate and the False Alarm Rate, exhibiting superior noise robustness and real-time performance.