Abstract: Based on deep learning with strong self-feature extraction capabilities and better classification capabilities, 1D-deep convolutional neural networks are used for bearing composite fault diagnosis. A method for bearing composite fault diagnosis based on deep convolutional neural networks is proposed. In the proposed method, the multi-fault vibration signal of the bearing is used as the direct input of the model. By training a deep convolutional neural network model, multiple convolution layers and pooling layers in the model are used to self-extract the input vibration signal. And classify the fault. Thus, an end-to-end composite fault diagnosis is formed in a data-driven manner. Experiments show that directly inputting bearing vibration signals in a deep convolutional neural network for fault diagnosis, compared with the method of extracting time and frequency domain features combined with support vector machines for fault diagnosis, the deep convolutional neural network can better reflect the time The relationship between domain vibration signals and features has achieved higher recognition efficiency than traditional intelligent diagnostic methods.