Graph Neural Network-Based Safety Evaluation and Anomaly Detection for Power Equipment Systems
Abstract
Ensuring safety and reliability in power equipment systems is critical for minimizing failures and maintaining operational efficiency. This paper introduces a novel safety evaluation and risk management framework leveraging Graph Neural Networks (GNNs). By modeling the intricate relationships among interconnected nodes in power systems, the GNN framework achieves high-precision safety score predictions, anomaly detection, and cascading failure analysis. Our model was trained and validated on a dataset comprising multi-dimensional sensor, failure, and maintenance records collected from over 1,000 equipment nodes, with more than 150,000 time-stamped entries. Experiments demonstrate that the proposed GNN framework achieves a mean accuracy of 88.9%, precision of 89.1%, recall of 87.6%, F1 score of 88.3%, and AUC-ROC of 0.93 across various hyperparameter settings. Compared to baseline methods such as traditional ML classifiers and CNN-LSTM models, the GNN exhibited superior performance in capturing spatial-temporal dependencies. The approach enables proactive identification of critical safety states and emerging risks, enhancing the resilience and reliability of complex power systems. This methodology bridges traditional safety evaluation techniques with graph-based learning, offering a scalable and intelligent solution for modern power equipment enterprises.References
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