关键词: 自编码, 深度学习, 故障诊断, 滚动轴承, 动态学习率

Abstract:

In order to obtain better convergence speed and classification accuracy of bearing fault classification, a Stacked Auto-Encoder（SAE） with dynamic adjustment of learning rate is proposed. In the following iteration, the gradient of the current reconstruction error is used to dynamically adjust the learning rate. According to the positive and negative value of the reconstruction error gradient, two different learning rate reduction strategies are given to make the learning rate more consistent with the current operation of the model. Finally, the accuracy of fault classification and recognition is verified by reverse fine-tuning of different labeled data quantities. The experimental results show that：compared with the fixed learning rate, the dynamic adjusted learning rate reduces 17.70% of the pre training convergence time, 22.92% of the reconstruction error, improves the accuracy of fault classification, and reduces the number of labeled samples on the premise of maintaining the accuracy of classification.

Key words: auto-encoder, deep learning, fault diagnosis, rolling bearing, dynamic learning rate