It is difficult to achieve satisfactory classification results for bearing fault diagnosis methods based on prior knowledge. This paper presents an adaptive convolution neural network based on Nesterov momentum for rolling bearing fault diagnosis. Firstly, the traditional momentum method in the network is replaced by Nesterov momentum. Nesterov momentum can predict the falling position of parameters and adjust the parameters in advance, to avoid the problem that the traditional momentum method is likely to miss the optimal solution. Secondly, in order to improve the generalization ability of the network, an adaptive learning rate rule which dynamically adjusts the learning rate according to the rate of error change is proposed. Finally, the original vibration signals are directly inputted into the proposed network to train the fault diagnosis model, and the test data are used to evaluate the model. The experimental results show that compared with the traditional convolutional neural network, the proposed method improves the convergence of the neural network and effectively improves the accuracy of bearing fault classification.

中文翻译：

---

基于Nesterov动量的自适应卷积神经网络的轴承故障诊断

基于先验知识很难对轴承故障诊断方法获得令人满意的分类结果。本文提出了一种基于Nesterov动量的自适应卷积神经网络，用于滚动轴承的故障诊断。首先，将网络中的传统动量方法替换为Nesterov动量。Nesterov动量可以预测参数的下降位置并提前调整参数，避免了传统的动量法可能错过最优解的问题。其次，为了提高网络的泛化能力，提出了一种自适应学习率规则，可以根据误差变化率动态调整学习率。最后，将原始的振动信号直接输入到建议的网络中，以训练故障诊断模型，并使用测试数据对模型进行评估。实验结果表明，与传统的卷积神经网络相比，该方法提高了神经网络的收敛性，有效地提高了轴承故障分类的准确性。