基于自适应本地融合联邦学习的电机轴承故障诊断  

Motor Bearing Fault Diagnosis Based on Adaptive Local Collaboration Federated Learning

作　　者：洪杨 吴钦木[1] HONG Yang;WU Qin-mu(School of Electrical Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学电气工程学院,贵阳550025

出　　处：《科学技术与工程》2025年第8期3217-3225,共9页Science Technology and Engineering

基　　金：国家自然科学基金(52267003)。

摘　　要：工业电机轴承的故障诊断对设备性能和寿命至关重要。传统的诊断方法是将多个工厂的数据汇集在一起,这存在数据隐私和标注成本高的问题。为了解决这些问题,提出一种基于自适应本地融合(adaptive local collaboration,ALC)联邦学习的故障诊断策略。在该方法中,不同工况轴承数据将存储于多个客户端,中心服务端与各个客户端协同工作,以建立联邦学习诊断模型。采用改进的ResNet-18网络作为分类器,在个性化联邦学习框架下进行训练,ALC联邦学习方法使每个客户端能有效融合全局和局部模型,提取全局信息优化本地训练结果。实验证明,该方法在保护数据隐私的同时与其他方法相比较,提高了故障诊断准确性,特别在多工厂环境中表现出更高的故障分类精度。Fault diagnosis of industrial motor bearings is crucial for equipment performance and lifespan.Traditional diagnostic methods aggregate data from multiple factories,leading to issues with data privacy and high annotation costs.To address these problems,a fault diagnosis strategy based on adaptive local collaboration(ALC)federated learning was proposed.In this approach,bearing data under different working conditions was stored across multiple clients,with a central server collaborating with each client to build a federated learning diagnostic model.An improved ResNet-18 network was used as the classifier,which was trained within the personalized federated learning framework.The ALC federated learning method enables each client to effectively integrate global and local models,extracting global information to optimize local training results.Experiments demonstrate that this method enhances fault diagnosis accuracy while protecting data privacy,showing higher fault classification precision compared to other methods,especially in multi-factory environments.

关 键 词：电机轴承 联邦学习(FL) 自适应本地融合(ALC) 故障诊断 

分 类 号：TH17[机械工程—机械制造及自动化]