Fault diagnosis is of great significance for ensuring the safety and reliable operation of rolling bearing in industries. Stack autoencoder (SAE) networks have been widely applied in this field. However, the model parameters such as learning rate are always fixed, which have an adverse effect on the convergence speed and accuracy of fault classification. Thus, this paper proposes a dynamic learning rate adjustment approach for the stacked autoencoder network. First, the input data is normalized and enhanced. Second, the structure of the SAE network is selected. According to the positive and negative value of the training error gradient, a learning rate reducing strategy is designed in order to be consistent with the current operation of the network. Finally, the fault diagnosis models with different learning rate adjustment are conducted in order to validate the better performance of the proposed approach. In addition, the influence of quantities of labeled sample data on the process of backpropagation is analyzed. The results show that the proposed method can effectively increase the convergence speed and improve classification accuracy. Moreover, it can reduce the labeled sample size and make the network more stable under the same classification accuracy.

中文翻译：

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基于动态学习率的堆叠式自动编码器网络的滚动轴承故障诊断

故障诊断对确保工业滚动轴承的安全可靠运行具有重要意义。堆栈自动编码器（SAE）网络已在该领域中得到广泛应用。然而，诸如学习率之类的模型参数始终是固定的，这对收敛速度和故障分类的准确性有不利影响。因此，本文提出了一种用于堆叠式自动编码器网络的动态学习速率调整方法。首先，对输入数据进行规范化和增强。其次，选择SAE网络的结构。根据训练误差梯度的正负值，设计了一种学习速率降低策略，以与网络的当前运行保持一致。最后，为了验证所提方法的性能，采用了不同学习率调整的故障诊断模型。另外，分析了标记的样本数据的数量对反向传播过程的影响。结果表明，该方法可以有效提高收敛速度，提高分类精度。而且，它可以减少标记的样本数量，并在相同的分类精度下使网络更稳定。