In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D‐CNN for rolling bearing fault diagnosis. First, 1‐dimension convolutional neural network (1D‐CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross‐entropy loss function and Adam optimizer are used to minimize the classification errors and the second‐order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

Transfer learning, leveraging the gaps through learned knowledge from source domain to target domain recognition, has achieved impressive performance in bearing fault diagnosis with two domains which have only weak distribution discrepancy. From different devices, collected vibration signal always suffered from big distribution discrepancy, which has limited the generalization of these existing transfer learning-based methods significantly. To overcome this challenge, the partial domain transfer learning methods are studied latest. However, most of these available techniques only focus on reducing the discrepancy of two domains using statistical distance metrics, which cannot consider the Riemannian manifold hidden in distribution space that impacted the accuracy of discrepancy measurement during testing. This paper proposes a Log-CORAL–based deep residual shrinkage network for partial domain adaptation bearing fault diagnosis scenario, where the domains data from different machinery are selected. Specifically, the log-correlation alignment (Log-CORAL), as a domain discrepancy metric, is explored to weaken the influence by Riemannian manifold, which disturbed the discrepancy measurement dependability. In addition, adversarial domain discriminator is embedded into deep residual shrinkage network to reduce the discrepancy between the two domains by maximizing the loss of domain discriminator. Comparison experiments with the SOTA methods on three well-known bearing datasets are conducted to verify effectiveness of the proposed method.