软件导刊2025，Vol.24Issue(9)：9-18,10.DOI:10.11907/rjdk.241564
复杂工况下的轴承故障诊断方法综述
Review of Bearing Fault Diagnosis Methods under Complex Working Conditions
摘要
Abstract
Bearing is an important part of rotating machinery.In practical applications,the operating environment of bearing is complex and changeable.It is a research hotspot to accurately judge the bearing fault under complex working conditions.Therefore,the sample imbalance in bearing fault diagnosis under complex working conditions and the transfer learning under variable working conditions are discussed.In the problem of sample imbalance,the advantages,disadvantages and applicable scenarios of the involved methods are analyzed from the perspec-tives of resampling technology and model-based generation method.In the part of transfer learning,the transfer method based on samples,fea-tures and parameters is explained in detail,and its application prospect in variable condition bearing fault diagnosis is discussed.In addition,it also looks forward to new technologies and methods that may emerge in the future,such as algorithms that combine deep learning and do-main adaptation to deal with more complex working conditions and data scenarios.The purpose is to provide reference for researchers in the field of bearing fault diagnosis,so as to further improve the accuracy and reliability of model diagnosis.关键词
复杂工况/轴承故障诊断/样本不平衡/迁移学习Key words
complex working conditions/bearing fault diagnosis/sample imbalance/transfer learning分类
信息技术与安全科学引用本文复制引用
马新娜,张策,李豪,何畔..复杂工况下的轴承故障诊断方法综述[J].软件导刊,2025,24(9):9-18,10.基金项目
国家自然科学基金项目(12172234) （12172234）
河北省自然科学基金项目(A2021210022) （A2021210022）
河北省"三三三人才"资助项目(A202101018) （A202101018）
