Rolling Bearing Fault Diagnosis Method Based on Enhanced Deep Auto-encoder Network

被引：4
|
作者：
Tong J. [1 ]
Luo J. [1 ]
Zheng J. [1 ]
机构：
[1] School of Mechanical Engineering, Anhui University of Technology, Ma'anshan
来源：
Zheng, Jinde (lqdlzheng@126.com) | 1600年 / Chinese Mechanical Engineering Society卷 / 32期
关键词：
Auto-encoder network; Deep learning; Fault diagnosis; Rolling bearing;
D O I：
10.3969/j.issn.1004-132X.2021.21.011
中图分类号：
学科分类号：
摘要：
To improve the feature mining capabilities of deep auto-encoder networks and select the network hyperparameters adaptively, an enhanced deep auto-encoder network was proposed for rolling bearing fault diagnosis. Maximum correlation entropy was used to replace mean square error as the loss function of auto-encoder. Sparse penalty term and contractive penalty term embedded with non-negative constraints were added to further reduce the reconstruction errors. Key parameters of the network were adaptively selected through gray wolf optimization algorithm. After experimental analyses, results show that compared with the existing methods, the proposed method has stronger feature extraction ability and stability. For bearing vibration data under variable operating conditions, the proposed method may also achieve high recognition accuracy. © 2021, China Mechanical Engineering Magazine Office. All right reserved.
引用
收藏
页码：2617 / 2624
页数：7
相关论文
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