A single-layer dense convolutional reversible residual network for bearing fault diagnosis based on differential local adaptive

Abstract For the cross-domain fault diagnosis of industrial bearings under different working conditions and noise, most current domain adaptation methods in transfer learning only focus on either marginal distribution alignment or conditional distribution alignment. They fail to adequately combine discriminative and global distribution information. Furthermore, the majority of models have a very high parameter count and memory utilization, which makes it challenging to use them in real-world industrial situations. Therefore, a single-layer densely connected reversible residual network based on differential local adaptation is proposed. This network is more competitive in industrial applications than other fault diagnosis models since it not only uses less memory and has fewer parameters, but it also shows superior cross-domain fault diagnostic capacity in noisy situations. Additionally, to extract discriminative and global domain-invariant features, a domain adaptation module is created that takes into account local and global data distributions differently. Multiple transfer tasks and two distinct datasets are used to validate the model. Comparative tests reveal that the suggested model uses less memory and requires fewer parameters to attain good accuracy and transferability.