Imbalanced Learning for Gearbox Fault Detection via Attention-Based Multireceptive Field Convolutional Neural Networks with an Adaptive Label Regulation Loss

Accurate gearbox fault identification is paramount for industrial production. In practice, gearboxes typically operate under normal conditions (rarely under faulty conditions), resulting in a long-tailed distribution of monitoring data. However, the majority of current algorithms are crafted based on the assumption of balanced sample distributions, which do not correspond with the prevalent conditions encountered in actual industrial settings. To cope with this challenge, an attention-based multireceptive field convolutional neural network (AMFCN) is established in this article. This study's main contributions can be summarized as follows: 1) we introduce a global contextual attention module (GCAM) to instruct the model to focus on learning ample features; 2) we establish a hierarchical receptive field module (HRFM) to incorporate powerful multilevel learning capabilities into the AMFCN model; and 3) we devise an adaptive label regulation loss (ALRL) to facilitate the model to obtain accurate fault identification results, particularly in situations with imbalanced data distributions. Two case studies show that the AMFCN model achieves 83.72% and 81.63% accuracy on two extremely imbalanced gearbox datasets, outperforming seven competitive algorithms.