A lightweight and robust model for engineering cross-domain fault diagnosis via feature fusion-based unsupervised adversarial learning

Cross-domain bearing fault diagnosis models have weaknesses such as large size, complex calculation and weak anti-noise ability. Hence, a lightweight and robust model via feature fusion-based unsupervised adversarial learning (LRFFUAL) is proposed, which could be a special benefit for practical engineering applications. A main innovation lies in a customized feature fusion block to achieve a tradeoff between model lightweight and robustness. Accordingly, a channel residual strategy is proposed to apply residual techniques for channels with weak feature information to achieve data augmentation. Concerning cross-domain tasks with huge distribution discrepancy, a new adversarial learning strategy is proposed to improve model convergence rate by inputting marginal features into a discriminator. Experimental results show that the proposed LRFFUAL has advantages of smaller size, less computation, and stronger robustness compared with other existing methods.