Fault diagnosis of bearing using Deep Neural Network with Dropconnect

被引：0
|
作者：
Lee, Jongkyu [1 ]
Lee, Donghee [1 ]
Kim, Byeongwoo [1 ]
机构：
[1] Ulsan Univ, Dept Elect Engn, 93 Daehak Ro, Ulsan, South Korea
来源：
PROCEEDINGS OF THE 2019 IEEE EURASIA CONFERENCE ON IOT, COMMUNICATION AND ENGINEERING (ECICE) | 2019年
基金：
新加坡国家研究基金会;
关键词：
Bearing fault diagnosis; deep neural network; dropconnect; Feature extraction;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Fault diagnosis of a rotating machine is one of the most important tasks from the perspective of system maintenance and repair. Recently, machine learning has been widely used as a fault diagnosis method for rotation machines. However, the algorithm has an overfitting problem. To solve this overfitting problem, this study proposes a fault diagnosis method for rotating machines by applying DropConnect. Useful features were extracted from vibration signals for fault detection and applied to Deep Neural Network(DNN) models.
引用
收藏
页码：530 / 533
页数：4
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