Objective To address information loss caused by manual feature extraction and improve the reliability of fault diagnosis beyond that of single-sensor approaches, this paper proposes a new method that integrates multi-sensor deep learning models.

Method In this method, sensors positioned at four different locations are first modeled as nodes within a graph structure. A parallel four-channel one-dimensional convolutional neural network (1DCNN) is then employed to extract deep features from the raw data of each node. Meanwhile, a dynamic adjacency matrix learning module is introduced to learn the spatial correlations among the nodes, so as to obtain the adjacency matrix of each node. Subsequently, a graph convolutional neural network (GCN) is used to fuse the deep features from all four nodes. Finally, the fused features are dimensionally reduced and classified to obtain the diagnostic results.

Results Experimental results show that the proposed multi-sensor fusion model achieves a fault diagnosis accuracy of over 98.82% on the marine screw pump dataset, significantly outperforming other comparative models.

Conclusion By fusing the deep features extracted by the parallel four-channel 1DCNN using a GCN, the model achieves substantial improvements in both diagnostic accuracy and fault tolerance.