Multiscale dilated convolution and swin-transformer for small sample gearbox fault diagnosis

Mechanical equipment usually operates in noisy and variable load environments, which presents serious challenges for existing intelligent diagnostic models. In addition, there are few labelled fault samples in real engineering scenarios, which makes it difficult to perform accurate fault identification for mechanical equipment. Thus, to solve the problem of diagnostic model performance degradation under small sample, noisy and variable load environments, this paper proposes a Multiscale Dilated Convolution and Swin-Transformer (MSDC-Swin-T) method for small sample gearbox fault diagnosis. First, we design the Coordinate Reconstruction Attention Mechanism (CRAM), which enhances the capture of impulse information by coordinate reconstruction. In addition, a multiscale convolutional token embedding module is constructed to extract local features at different scales, and its ability for capturing important features is adaptively enhanced by CRAM. Then, Swin-Transformer is utilized for modeling global dependencies, thus mining more subtle fault features. Finally, the effectiveness and stability of the MSDC-Swin-T is proved on two gearbox datasets. The experiments show that MSDC-Swin-T has superior diagnostic performance under small sample with noise and variable load environments. The diagnostic accuracy is better than the state-of-the-art methods.