Dual-Path Fault Diagnosis of Small Sample for Mechanical Systems Based on Multiple Attention Mechanisms

For fault diagnosis, it is important to effectively leverage the inherent characteristics of small datasets, but it is rarely considered in many the existing deep learning approaches. To this end, a dual-path network model based on multiple attention mechanisms is proposed in this work. The proposed model enriches the features of small sample by combining one-dimensional (1-D) frequency signals with two-dimensional (2-D) time-frequency images. A 1-D attention mechanism is applied in the 1-D frequency extraction path to focus classification on sensitive frequency information, while an improved global attention mechanism is added to the 2-D feature extraction path, which refines the key features and reduces interference resulting from noise in the image data. Moreover, the stability of the learning process is enhanced through the application of a combinatorial loss function composed of label smoothing regularization and gradient harmonizing mechanism loss functions. Finally, the diagnostic performance of the proposed method is validated using two different public fault diagnosis datasets in comparison with the state-of-the-art methods.