机电工程技术2026，Vol.55Issue(5)：17-23,7.DOI:10.3969/j.issn.1009-9492.2025.00069
基于LMMD-DANN的无监督风电轴承故障诊断方法
Unsupervised Fault Diagnosis Method for Wind Turbine Bearings Based on LMMD-DANN
摘要
Abstract
The health status of the rolling bearings of wind turbines directly affects the performance of the equipment,but under the condition of variable working conditions,the target domain data is often missing labels,and its diagnostic performance will be greatly reduced.An LMMD-DANN fault diagnosis model is proposed to solve this problem.The model uses a multi-module ensemble architecture that combines a feature extraction network,a local maximum mean difference(LMMD)algorithm,and a domain adversarial neural network(DANN).That is,by introducing the domain adversarial mechanism,an adversarial relationship is established between the feature extractor and the domain classifier,which enhances the feature extraction ability and realizes the feature confusion of cross-domain data.At the same time,the LMMD algorithm is used to focus on local features,which promotes the feature alignment of similar subdomains.The adaptive weighting strategy is introduced to dynamically adjust the proportion of domain classification loss in the loss function.The experimental results on the CWRU and JNU bearing datasets show that the average accuracy of the proposed method in the six variable working conditions reaches 98.88%and 89.1%,respectively,so LMMD-DANN has more advantages in the unsupervised fault diagnosis scenarios of variable working conditions.关键词
轴承故障诊断/迁移学习/变工况/域对抗Key words
bearing fault diagnosis/transfer learning/variable condition/domain adversarial分类
信息技术与安全科学引用本文复制引用
王萌璠,蔡宗琰,田心平,周昌..基于LMMD-DANN的无监督风电轴承故障诊断方法[J].机电工程技术,2026,55(5):17-23,7.基金项目
国家自然科学基金(51705428) （51705428）
