A dual-view network for fault diagnosis in rotating machinery using unbalanced data.

Data-driven intelligent methods have demonstrated their effectiveness in the area of fault diagnosis. However, most existing studies are based on the assumption that the distributions of normal and faulty samples are balanced during the diagnostic process. This assumption significantly decreases the application range of a diagnostic model as the samples in most real-world scenarios are highly unbalanced. To cope with the limitations caused by unbalanced data, this paper proposed an original dual-view network (DVN). Firstly, an interactive graph modeling strategy is introduced for relationship information modeling of multi-sensor data. Meanwhile, the graph convolution operation is used as the baseline for feature extraction of the constructed interactive graph to mine for fault representations. Secondly, an original dual-view classifier consisting of a binary classifier and a multi-class classifier is proposed, which divides fault diagnosis into two stages. Specifically, in the first stage, the binary classifier performs the binary inference from the view of fault detection. In the second stage, the multi-class classifier performs the full-state inference from the view of fine-grained fault classification. Then, based on the dual-view classifier, a weight activation module is designed to alleviate training bias toward majority classes by sample-level re-weighting. Finally, the diagnosis results can be obtained according to the output of the multi-class classifier. Fault diagnosis experiments using two different datasets with varying data unbalance ratios were conducted to validate the effectiveness of the proposed method. The superiority of the proposed DVN is verified through comparisons with state-of-the-art methods. The effectiveness of the DVN is further validated through ablation studies with some ablative models. The DVN code is available at: https://github.com/CQU-ZixuChen/DualViewNetwork. [ABSTRACT FROM AUTHOR]