An improved prototype network method for small sample bearing fault diagnosis

被引：0
|
作者：
Zhao Z. [1 ,2 ]
Zhang R. [2 ]
Liu K. [2 ]
Yang S. [1 ]
机构：
[1] State Key Laboratory of Mechanical Behavior in Traffic Engineering Structure and System Safety, Shijiazhuang Tiedao University, Shijiazhuang
[2] School of Computation and Informatics, Shijiazhuang Tiedao University, Shijiazhuang
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2023年 / 42卷 / 20期
关键词：
Auxiliary task; Fault diagnosis; Few-shot learning; Prototype network;
D O I：
10.13465/j.cnki.jvs.2023.20.025
中图分类号：
TH13 [机械零件及传动装置];
学科分类号：
080203 ;
摘要：
In the fault diagnosis of prototype network with small samples, the accuracy of the prototype is not very good because of the small number of fault samples and the influence of outliers. In order to improve the accuracy of fault prototype representation, a small sample fault diagnosis method based on improved prototype network is proposed in this paper. By introducing an auxiliary classification task to extract more robust features, the distinguishing ability of extracted features is improved. In addition, the query set sample is used to further optimize the class prototype to improve the representation ability of the class prototype to the fault bearing. To verify the effectiveness of the proposed method, set K to different values, and conduct C-way K-shot fault diagnosis experiments on the rolling bearing data set. The experimental results show that the prototype obtained by the improved prototype network has better discrimination and accuracy. In the 10 way 5-shot experiment, the accuracy of the proposed method is 5.1% higher than that of the traditional prototype network. © 2023 Chinese Vibration Engineering Society. All rights reserved.
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收藏
页码：214 / 221
页数：7
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