Research on bearing condition monitoring based on deep learning

被引：0

作者：

Guo L. ^{[1
]}

Gao H.-L. ^{[1
]}

Zhang Y.-W. ^{[1
]}

Huang H.-F. ^{[1
]}

机构：

[1] College of Mechanical Engineering, Southwest Jiaotong University, Chengdu

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2016年 / 35卷 / 12期

关键词：

Antifriction bearing; Condition-based maintenance; Deep learning; Unsupervised learning;

D O I：

10.13465/j.cnki.jvs.2016.12.026

中图分类号：

学科分类号：

摘要：

The vibration signal of rolling element bearing occurs with a high degree of coupling, which means that the features and recognition model are difficult to build. For solving these problems, we proposed a novel bearing condition-monitoring model based on deep learning. Time domain, frequency domain and time-frequency domain features are extracted. Then these feature vectors are entered into an unsupervised auto-encoder to learn the high-level features. At the same time, the middle layers of the auto-encoder network are stacked into a multilayered network. Finally, a small number of labeled training samples are used to fine-tune the deep learning network. The bearing condition recognition experiment shows that the proposed method achieves state-of-the-art results, and its high accuracy in terms of the performance degradation condition is very helpful when it comes to condition-based maintenance. © 2016, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：166 / 170and195

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