The model-based and signal-based rolling bearing fault diagnosis methods have problems such as difficult modeling and cumbersome signal analysis. The data-driven rolling bearing fault diagnosis methods mostly use convolutional neural networks, but as the number of network layers increases during network training, gradient disappearance occurs. Moreover, taking the vibration signal of the rolling bearing directly as the network input will cause incomplete feature extraction. In order to solve the above problems, a rolling bearing fault diagnosis method based on Gramian angular field(GAF) and densely connected convolutional network(DenseNet) is proposed. The one-dimensional time series of rolling bearing vibration signals are converted into two-dimensional images by GAF, which preserves the correlation information between the time series data. The two-dimensional images are used as the input of the densely connected convolutional network, and the feature extraction of the two-dimensional images is carried out by the DenseNet, which improves the feature information utilization and realizes the fault classification. Experiments are carried out by using the data from the Case Western Reserve University bearing dataset. The results show that the method can identify rolling bearing fault types effectively with a fault diagnosis accuracy rate of 99.75%. In order to further prove the superiority of this method, the fault diagnosis methods of gray-scale image+DenseNet, GAF+residual network(ResNet), gray-scale image + ResNet are selected for comparison. The results show that the GAF+DenseNet method has the highest accuracy rate, and the gray-scale image+ResNet method has the lowest accuracy rate. Compared with the gray-scale image, the GAF converted two-dimensional image retains the relevant information between the original time series data. Compared with ResNet, DenseNet is able to extract the fault features more adequately due to denser connection method.
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