无源数据约束下多源域自适应的风电齿轮箱故障诊断方法  

MULTI-SOURCE DOMAIN ADAPTIVE FAULT DIAGNOSIS METHOD OF WIND TURBINE GEARBOX UNDER NO-ACCESSING SOURCE DATA CONSTRAINTS

作　　者：吴宣勇 黄忠全 李琪康 汤宝平[1] Wu Xuanyong;Huang Zhongquan;Li Qikang;Tang Baoping(State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学机械传动国家重点实验室,重庆400044

出　　处：《太阳能学报》2024年第4期238-246,共9页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(52275087);国家重点研发计划(2020YFB1709800)。

摘　　要：针对在数据隐私和安全性的背景下,无法接触源域数据导致领域自适应方法不可用的问题,提出一种无源数据约束下多源域自适应的故障诊断方法。首先,通过信息最大化损失促使源域与目标域数据在特征空间进行对齐;然后利用自监督伪标签策略挖掘目标域数据的特征表征信息,并采用熵筛选策略抑制噪声伪标签的影响;最后通过自适应加权有效利用多个源域的知识并抑制负迁移影响,实现无源数据约束下的风电齿轮箱的故障诊断。通过动力传动综合实验台数据和某风场风电机组CMS数据对所提方法进行验证与应用。结果表明:所提方法仅利用预训练的源域模型和目标域无标签数据即可有效实现目标域风电齿轮箱故障诊断。In the context of data privacy and security,domain adaptive methods is unavailable due to inaccessibility of source domain data.A multi-source domain adaptive fault diagnosis method under no-accessing source data constraints is proposed.Firstly,the source and target domain data are aligned in the feature space by information maximization loss.Then the feature representation information of the target domain data is further mined using the self-supervised pseudo-label strategy,and the influence of noise pseudo-labels is suppressed using the entropy filtering strategy.Finally,the knowledge of multiple source domains is effectively utilized and the influence of negative transfer is suppressed to realize the fault diagnosis of wind turbine gearbox under no-accessing source data constraints through adaptive weighting.This method is applied and verified using the drivetrain dynamic simulator test bench data and the wind turbine CMS data of a wind farm.The results show that the proposed method can effectively realize the fault diagnosis of wind turbine gearbox in the target domain using only the pre-trained source domain model and the unlabeled data of the target domain.

关 键 词：风电机组 数据隐私 自适应算法 无源数据约束 齿轮箱 故障诊断 

分 类 号：TH17[机械工程—机械制造及自动化]