Fault Diagnosis of Gearbox Based on Cross-Domain Transfer Learning With Fine-Tuning Mechanism Using Unbalanced Samples

In gearbox fault diagnosis, the performance of diagnostic accuracy, generalization capability in data-driven diagnostic models is greatly impacted due to imbalanced small datasets. Besides, significant time investment for hyperparameters tuning of diagnostic model makes it difficult for classifier to obtain an accurate identification. To tackle the above issues, in this article, a novel method named cross-domain transfer learning with fine-tuning mechanism (CTL-FTM) is proposed for fault diagnosis of gearbox using unbalanced samples, and the fault features can be extracted with pretraining model and fault types can be recognized with shallow network. Two fault datasets of gearbox are employed to verify the effectiveness of the proposed algorithm. Experimental results demonstrate that the proposed CTL-FTM algorithm is superior than state-of-the-art benchmark transfer learning methods such as pure VGG16 and VGG16 with attention mechanism models, in terms of convergence speed, recognition accuracy, loss rates, time consumption, and generalization ability.