计算机工程与应用2024，Vol.60Issue(15)：42-54,13.DOI:10.3778/j.issn.1002-8331.2401-0112
零小样本旋转机械故障诊断综述
Review on Zero or Few Sample Rotating Machinery Fault Diagnosis
摘要
Abstract
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 prob-lems,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.关键词
零样本/小样本/故障诊断/数据扩充Key words
zero samples/few samples/fault diagnosis/data expansion分类
矿业与冶金引用本文复制引用
刘俊孚,岑健,黄汉坤,刘溪,赵必创,司伟伟..零小样本旋转机械故障诊断综述[J].计算机工程与应用,2024,60(15):42-54,13.基金项目
广东省普通高校创新团队项目(2020KCXTD017) （2020KCXTD017）
广州市科技重点研发计划(202206010022). （202206010022）
