计量学报2024，Vol.45Issue(9)：1360-1369,10.DOI:10.3969/j.issn.1000-1158.2024.09.14
自监督学习结合对抗迁移的跨工况轴承故障诊断
Self-supervised Learning Combined with Adversarial Transfer for Cross-conditions Bearing Fault Diagnosis
摘要
Abstract
In the application of intelligent bearing fault diagnosis,it is extremely difficult to obtain sufficient real fault data due to the complexity and variability of the actual working conditions,and there exist large differences between the signals in the target and source domains,leading to the problems of difficult feature extraction and classification and weak generalization of the model in the cross-working condition transfer recognition of the deep model.Considering the existence of a large amount of unlabelled data in the target domain,an unsupervised ideas and proposed an improved method based on self-supervised learning combined with adversarial transfer is introduced.Firstly,the pretext tasks are created based on the characteristics of the signal itself to learn from a large amount of unlabeled data and establish the intrinsic connections of fault categories between the source domain and the target domain;then the knowledge learned from the source domain is transferred to the target domain through adversarial domain adaptation and joint maximum mean difference,and finally,it is combined with the pretext task to optimize the difference between the two domains and achieved accurate fault classification in the target domain.Experimental results have verified the feasibility and good performance,accuracy is all higher than 98％in most cases.关键词
轴承故障诊断/自监督学习/跨工况/对抗迁移Key words
bearing fault diagnosis/self-supervised learning/cross working conditions/adversarial transfer分类
通用工业技术引用本文复制引用
温江涛,刘仲雨,孙洁娣,时培明..自监督学习结合对抗迁移的跨工况轴承故障诊断[J].计量学报,2024,45(9):1360-1369,10.基金项目
国家自然科学基金(61973262) （61973262）
河北省自然科学基金(E2020203061) （E2020203061）
河北省重点研发计划项目(22371801D) （22371801D）
河北省重点实验室项目(202250701010046) （202250701010046）
