Richly connected spatial-temporal graph neural network for rotating machinery fault diagnosis with multi-sensor information fusion

Intelligent fault diagnosis has become increasingly relevant in predictive maintenance for rotating machinery. With advancements in data transmission and sensor technology, measurement systems can now gather vast amounts of data from multiple sensors. These multi-sensor datasets are multivariate time series with significant Spatial-temporal correlation. Utilizing this correlation to achieve accurate diagnostics is a significant challenge. To fully leverage the Spatial-temporal correlations, especially the correlations among different sensors at various time steps, we propose a new, richly connected Spatial-temporal graph neural network for diagnosing faults in rotating machinery. This network primarily comprises two modules: graph construction and graph diffusion convolution with pooling. The graph construction module initially builds a richly connected graph that considers both the connections among sensors at the same time step and the connections between sensors across different time steps. Subsequently, we design a attenuation matrix that takes into account temporal distances to adjust the connection strengths between sensors based on their time separation. By applying Graph Diffusion Convolution (GDC) on the constructed graph, information can be propagated among nodes within a broader neighborhood, even capturing the interactions between nodes across multiple time steps. By combining GDC with pooling operations, temporal and spatial dependencies can be effectively captured to learn efficient representations. We evaluated the effectiveness of our approach through comparative experiments on three datasets, challenging various methods. The results demonstrate our method's superior capability in integrating Spatial-temporal features thoroughly.