CapsFormer: A Novel Bearing Intelligent Fault Diagnosis Framework With Negligible Speed Change Under Small-Sample Conditions

In actual industrial production, the load and speed of bearings are complex and changeable. However, most existing research on bearing fault diagnosis is based on constant speed conditions, and studies on bearing fault diagnosis at time-varying speeds are limited. Additionally, the scarcity of fault data further hinders practical applications of theoretical models developed so far. Thus, CapsFormer, a novel bearing intelligent fault diagnosis framework with negligible speed change under small-sample conditions, is proposed in this study. This framework combines the power of capsule network (CapsNet) and Transformer. It converts 1D time-domain samples into 2D time-frequency representations (TFRs) through short-time Fourier transform (STFT). Then it employs the idea of CapsNet to extract ordered spatial features from the TFRs of samples. On this basis, combined with the self-attention learning mechanism, it excavates deep fault features to promote the correct identification of bearing fault types by the model. Through experiments conducted under constant speed and time-varying speed conditions, the model was validated, demonstrating its superior performance compared to six other deep learning methods in bearing fault diagnosis under small sample sizes. These results strongly indicate the robustness of CapsFormer in addressing speed changes during bearing fault diagnosis.