Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (20): 264-269.DOI: 10.3778/j.issn.1002-8331.1909-0243

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Roller Bearing Fault Diagnosis Based on Stacked Auto-encoder with Dynamic Learning Rate

TANG Wei, ZHENG Yuan, PAN Hong, XU Jingjun   

  1. 1.College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
    2.Institute of Innovation, Hohai University, Nanjing 210098, China
    3.College of Energy and Electrical, Hohai University, Nanjing 211100, China
  • Online:2020-10-15 Published:2020-10-13



  1. 1.河海大学 水利水电学院,南京 210098
    2.河海大学 创新研究院,南京 210098
    3.河海大学 能源与电气学院,南京 211100


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



关键词: 自编码, 深度学习, 故障诊断, 滚动轴承, 动态学习率