SCML-GNN: A Graph Neural Network Model Leveraging Sensor Causality and Meta-Learning for Mechanical Fault Classification

Fault classification of mechanical equipment is a vital issue in modern industrial production. Mechanical equipment typically relies on multiple sensors to collect the operational data, which is represented as multivariate time-series data. However, existing methods for analyzing mechanical faults often overlook the causal relationships between sensors and struggle with the scarcity of labeled training samples. To address these challenges, we propose a graph neural network model leveraging sensor causality and meta-learning for mechanical fault classification (SCML-GNN). Specifically, we use transfer entropy to represent multivariate time-series data as a graph, with each sensor as a node and their causal relationships as edges. We then extract the node features using temporal convolutional layers and apply a graph neural network to learn the low-dimensional features. Additionally, graph pooling methods are used to obtain global embeddings. To further tackle the issue of limited labeled training samples, we introduce a metric-based class prototype attention mechanism within SCML-GNN. Extensive experiments conducted on three real-world mechanical equipment datasets demonstrate the superior effectiveness and efficiency of SCML-GNN in mechanical fault classification compared to the other existing methods.