Multisource Heterogeneous Information Fusion Based on Graph Convolutional Network for Gearbox Fault Diagnosis

Achieving information fusion of multisensor data plays an important role in improving the performance of gearbox fault diagnosis. However, this fusion process is hindered by the heterogeneity problem caused by the different data dimensions of various sensors. To solve this problem, exploitation of the complementary nature of multisource heterogeneous data to provide more accurate fault information is necessary. Thus, a multisource heterogeneous information fusion method-based graph convolutional network (MHIF-GCN) is proposed in this article. In this framework, a convolutional autoencoder (CAE) is used to extract deep features corresponding to different types of sensors as graph node features for solving data heterogeneity problems. Second, the graph convolutional network (GCN) model based on K-nearest neighbor graph (KNNGraph) is introduced to establish the connection between different sensor data in the graph structure for realizing the feature-level fusion of sensor data and mining deeper fault data features. The results of two gearbox experiments validate the excellent fault diagnosis performance of the proposed MHIF-GCN. In Experiment I, the MHIF-GCN can accurately recognize six structural and nonstructural fault types. With the support of the complementary fusion mechanism, the proposed MHIF-GCN has the highest average diagnostic accuracy of 99.00% when compared with the other six methods. Even with a small number of training samples, the MHIF-GCN still performs very favorably compared to other methods with an accuracy of 88.87%. In Experiment II, the MHIF-GCN has the highest diagnostic accuracy of 94.00%, and the recall, precision, and the F-score for each fault state remain above 85%, and the proposed MHIF-GCN maintains a stable diagnostic performance.