Review on Zero or Few Sample Rotating Machinery Fault Diagnosis

被引：0
|
作者：
Liu, Junfu [1 ,2 ]
Cen, Jian [1 ,2 ]
Huang, Hankun [1 ,2 ]
Liu, Xi [1 ,2 ]
Zhao, Bichuang [1 ,2 ]
Si, Weiwei [1 ,2 ]
机构：
[1] School of Automation, Guangdong Polytechnic Normal University, Guangzhou,510665, China
[2] Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory, Guangzhou,510665, China
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 15期
关键词：
With the advent of the data era; data-driven fault diagnosis methods have demonstrated excellent performance. Since the application of deep learning in fault diagnosis; supervised learning has made significant advancements. However; when samples are scarce or missing; supervised learning lacks the necessary training conditions. This paper proposes the zero-shot and small-sample problem; and analyzes its current status in the field of rotating machinery fault diagnosis. It reviews the development process; mainstream models; and current research hotspots of zero-shot rotating machinery fault diagnosis. Existing research achievements are summarized from two aspects: zero-shot problems and small-sample problems; and their applications in zero-shot and small-sample problems are analyzed. Finally; the paper discusses the future trends in zero-shot methods for rotating machinery fault diagnosis. © 2024 Journal of Computer Engineering and Applications Beijing Co; Ltd; Science Press. All rights reserved;
D O I：
10.3778/j.issn.1002-8331.2401-0112
中图分类号：
学科分类号：
摘要：
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页码：42 / 54
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