Deep learning has been widely used in intelligent fault diagnosis field in the era of industrial internet due to its excellent big data analysis ability. However, it lacks the specific attention enhancement mechanism in the training process, and is difficult to be applied to the speed transfer fault diagnosis under unbalanced training sample condition. Considering these challenges, a neural network called discrete-peak joint attention enhancement (DPJAE) convolutional model is proposed. First, the proposed discrete channel attention enhancement method is applied to enhance the characteristic discreteness of convolutional neural network channels. Then, the peak channel attention enhancement method follows, which is employed to enhance feature peaks in convolutional neural network channels. Finally, the discrete-peak joint attention enhancement method is placed in the middle of a well-designed robust CNN architecture to enhance the discreteness and peak value for channel characteristics. In order to avoid the over-fitting phenomenon of high variance in the training process of the deep network model, $L_2$ regularization method is introduced to regularize the weights for the front layer network. The diagnosis results on the open test dataset show that the DPJAE model not only gets high accuracy in the unbalanced training samples, but also achieves high performance in the rotating speed transfer testing samples. The proposed method is also verified on the private dataset. Compared with other diagnostic methods, the method has better diagnostic results.

深度学习以其出色的大数据分析能力已被广泛应用于工业互联网时代的智能故障诊断领域。但是，它在训练过程中缺乏专门的注意力增强机制，难以在训练样本不均衡的情况下应用于速度传递故障的诊断。考虑到这些挑战，提出了一种称为离散峰联合注意力增强（DPJAE）卷积模型的神经网络。首先，将所提出的离散信道注意力增强方法应用于增强卷积神经网络信道的特征离散性。然后，遵循峰通道注意力增强方法，该方法用于增强卷积神经网络通道中的特征峰。最后，离散峰联合注意力增强方法放置在精心设计的健壮的CNN架构中间，以增强信道特性的离散性和峰值。为了避免在深度网络模型的训练过程中出现高方差的过度拟合现象，${\mathrm{大号}}_2$引入正则化方法来正则化网络的权重。在开放测试数据集上的诊断结果表明，DPJAE模型不仅在不平衡训练样本中具有较高的精度，而且在转速传递测试样本中也具有较高的性能。私有数据集上也验证了所提出的方法。与其他诊断方法相比，该方法具有更好的诊断效果。