Fault diagnosis of bearings based on an improved lightweight convolution neural network

被引：0
作者：
Li, Qiankun [1 ]
Cui, Mingliang [1 ]
Wang, Youqing [1 ]
机构：
[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing, Peoples R China
来源：
2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS | 2023年
关键词：
Fault diagnosis; Light weight; Convolutional neural network; Cost;
D O I：
10.1109/DDCLS58216.2023.10166950
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Great progresses have been made in fault diagnosis of bearings based on convolutional neural networks, but these models bring a significant burden on the hardware, increase industrial costs, and inconvenience to the updating and training of models. A good fault diagnosis model should have a low number of parameters and be able to achieve high accuracy. In order to better reduce the number of network parameters while maintaining high accuracy, this study proposes a lightweight network model that can solve both of these problems through experimental comparison.
引用
收藏
页码：202 / 207
页数：6
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