As multi-sensor technology advances and data storage and processing capabilities improve, multi-sensor signals can provide a more comprehensive and multi-angle data perspective. This improves the reliability of fault diagnosis. The data between multiple sensors belongs to non Euclidean data. Traditional neural network methods can only extract signal features within a single sensor. It cannot handle non Euclidean data and cannot fully utilize the structural information and redundancy between sensors. On the other hand, graph neural network methods can handle non Euclidean data, but most graph data inputs lack practical physical meaning. To address this issue, this paper presents a graph construction method based on space and similarity, which models multi-sensor data as graph data. This method can fully utilize the structural features between sensors, giving the graph data practical physical meaning. Then graph fusion is performed on a batch of graph data samples. And the DeeperGCN model is applied to extract signal features, achieving recognition of the health status of aircraft engine bearings. Experiments have shown that this method can significantly improve the effectiveness of fault diagnosis.

fault diagnosis,graph neural network,multi-sensor,aeroengine bearings