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In order to solve the problem that the data distribution of the source domain and the target domain of rolling bearing is different in the variable working condition environment and the samples of the target domain do not contain labels, a fault diagnosis model of the rolling bearing based on the deep adaptive transfer learning network (DATLN) is proposed. Firstly, a domain-shared characteristic extraction network is built, and multiscale convolutional neural network (MSCNN) is used to suppress noise interference, so as to effectively extract local fault information contained in vibration signals. Secondly, combined with a bi-directional long short-term memory network (BiLSTM), the temporal characteristics in the local fault information are further learned. Finally, transfer learning is introduced to build a domain adaptive module with domain adversarial (DA) training combined with adaptive joint distribution (AJD) metrics. By maximizing the domain classification loss and minimizing the AJD distance, the source and target domain characteristic samples are aligned. The anti-noise experiment and transfer experiment are carried out on the open source CWRU data set and the measured data set of the mechanical fault platform respectively. The anti-noise experiments show the following points. ① The identification accuracy of MSCNN-BiLSTM network is above 99% in the noise-free environment, which shows that MSCNN-BiLSTM network has a good characteristic extraction capability. ② The identification accuracy of MSCNN-BiLSTM, LeNet-5, MSCNN and BiLSTM decreases with the increase of noise intensity. ③ Under the noise environment of 3, 5 and 10 dB, the average identification accuracy of MSCNN-BiLSTM network is higher than that of LeNet-5, MSCNN and BiLSTM networks, indicating that MSCNN-BiLSTM network has better anti-noise interference performance. ④ The MSCNN-BiLSTM network converges first with less fluctuation in both the noise-free environment and the 3 dB noise environment. The transfer experiments show the following points. ① The average identification accuracy of DA+AJD method is 97.36% on unlabeled target domain dataset, which is higher than that of Baseline, transfer component analysis(TCA) and domain adversarial neural network (DANN). ② On the test set confusion matrix, only one sample of the DA+AJD method is incorrectly identified, indicating that the DA+AJD method based on domain adaptation has better fault transfer diagnosis performance. ③ The t-SNE algorithm is used to visualize the processed source and target domain characteristic samples. The DA+AJD method only has a small number of rolling element fault and outer ring fault characteristic samples in the target domain that are incorrectly aligned to the inner ring fault characteristic samples area in the source domain. This result indicates that the DA+AJD method can effectively reduce the edge distribution and conditional distribution differences between the source domain and the target domain, and thus achieves better characteristic sample alignment.

To address the problems of existing 2D image-based imbalanced fault diagnosis methods for rolling bearings, which generate images with inadequate texture details and color degradation, this paper proposes a novel image enhancement model based on a dual-branch generative adversarial network (GAN) combining spatial and frequency domain information for an imbalanced fault diagnosis of rolling bearing. Firstly, the original vibration signals are converted into 2D time–frequency (TF) images by a continuous wavelet transform, and a dual-branch GAN model with a symmetric structure is constructed. One branch utilizes an auxiliary classification GAN (ACGAN) to process the spatial information of the TF images, while the other employs a GAN with a frequency generator and a frequency discriminator to handle the frequency information of the input images after a fast Fourier transform. Then, a shuffle attention (SA) module based on an attention mechanism is integrated into the proposed model to improve the network's expression ability and reduce the computational burden. Simultaneously, mean square error (MSE) is integrated into the loss functions of both generators to enhance the consistency of frequency information for the generated images. Additionally, a Wasserstein distance and gradient penalty are also incorporated into the losses of the two discriminators to prevent gradient vanishing and mode collapse. Under the supervision of the frequency WGAN-GP branch, an ACWGAN-GP can generate high-quality fault samples to balance the dataset. Finally, the balanced dataset is utilized to train the auxiliary classifier to achieve fault diagnosis. The effectiveness of the proposed method is validated by two rolling bearing datasets. When the imbalanced ratios of the four datasets are 0.5, 0.2, 0.1, and 0.05, respectively, their average classification accuracy reaches 99.35% on the CWRU bearing dataset. Meanwhile, the average classification accuracy reaches 96.62% on the MFS bearing dataset. [ABSTRACT FROM AUTHOR]