Bearing Fault Diagnosis Method Based on Complementary Feature Extraction and Fusion of Multisensor Data

Bearing is the key component of rotating machinery, so the fault diagnosis of bearing is important to improve the reliability of equipment operation. In recent years, the feature fusion method has been extensively explored in the fault diagnosis of bearings. However, the complementary fault features from multisensor data are difficult to be fully extracted, which will lead to the failure of achieving the expected diagnostic accuracy. This article proposes a multitask network for bearing fault diagnosis. The multihead attention is improved by 1-D convolutional neural network (CNN) to extract the deep features of multisensor data. The task of feature source discrimination allows the extracted features to contain complementary fault information as much as possible. Based on the complementary fault features, the accuracy of the fault category classification task can be greatly improved. To verify the effectiveness of the proposed method, the experiments are conducted on Paderborn bearing data set. The results show that the accuracy of the proposed method is greatly improved, which is much higher than the other methods.