Fault classification of rolling bearing based on attention mechanism-inception-CNN Model
ZHU Hao1, NING Qian1,2, LEI Yinjie1, CHEN Bingcai3, YAN Hua1
1.School of Electronic Information Engineering, Sichuan University, Chengdu 610065, China;
2.School of Physics and Electronic Engineering, Xinjiang Normal University, Urumqi 830054, China;
3.School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
Abstract：Aiming at traditional machine learning method requiring a lot of expert knowledge and high economic costs, a convolutional neural network (CNN) based on attention mechanism and Inception network structure was proposed. Its attention mechanism was used to assign different weights to different feature dimensions of data, extract more critical and important information, and make the model do more accurate judgments. Its Inception network structure was used to broaden the network width, increase the network’s adaptability to the convolution core scale, and extract more abundant features. In order to improve the generalization ability of the model, a DropBlock layer was added to follow each convolution layer and full connection layer. Finally, the results showed that the proposed model can not only achieve higher fault classification accuracy and stability under the same load, but also maintain higher accuracy and stability under different load conditions and rolling bearing data sets with different scales.
朱浩1，宁芊1,2，雷印杰1，陈炳才3，严华1. 基于注意力机制-Inception-CNN模型的滚动轴承故障分类[J]. 振动与冲击, 2020, 39(19): 84-93.
ZHU Hao1, NING Qian1,2, LEI Yinjie1, CHEN Bingcai3, YAN Hua1. Fault classification of rolling bearing based on attention mechanism-inception-CNN Model. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(19): 84-93.
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