基于改进域对抗网络的齿轮箱跨工况故障诊断  

Gearbox fault diagnosis across different operating conditionsbased on improved domain-adversarial network

作　　者：贾宝惠[1] 苏家成 高源 Jia Baohui;Su Jiacheng;Gao Yuan(School of Transportation Science and Engineering,Civil Aviation University of China,Tianjin 300300,China;School of Safety Science and Engineering,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学交通科学与工程学院,天津300300 [2]中国民航大学安全科学与工程学院,天津300300

出　　处：《电子测量技术》2025年第3期83-91,共9页Electronic Measurement Technology

基　　金：国家自然科学基金委员会-中国民用航空局联合研究基金(U2033209);中央高校基本科研业务费(KJZ53420240023)项目资助。

摘　　要：针对不同工况下采集的齿轮箱振动数据特征分布不一致和噪声成分影响迁移效果的问题,本文提出了一种结合注意力机制的域对抗迁移网络的深度迁移学习故障诊断方法。首先,将带标签的振动信号和未带标签的振动信号通过固定长度的数据分割方法构建成数据集;其次,为减少噪声样本带来的负迁移影响,采用卷积注意力模块(CBAM)以及判别损失项辅助特征提取器提取具有区分度的特征,加强分类决策边界;最后,为解决数据特征分布不一致的问题,采用多核最大均值差异(MK-MMD)对齐源域和目标域的全局分布,并利用对抗机制对齐两域的子领域分布。在公开的变工况齿轮箱故障数据集上进行试验验证,结果表明,所提方法的平均识别准确率达到96.25%以上,并通过与其他诊断方法的对比分析,验证了所提方法的有效性和优越性。To address the issues of inconsistent feature distributions and the influence of noise components on the transfer effect in gearbox vibration data collected under different operating conditions,this paper proposes a deep transfer learning fault diagnosis method that integrates an attention mechanism with domain adversarial transfer networks.First,labeled and unlabeled vibration signals are constructed into datasets using a fixed-length data segmentation method.Second,to reduce the negative transfer impact caused by noisy samples,a convolutional block attention module(CBAM)and a discriminative loss term are used to assist the feature extractor in extracting discriminative features and enhancing the classification decision boundary.Finally,to solve the problem of inconsistent data feature distributions,a multi-kernel maximum mean discrepancy(MK-MMD)is employed to align the global distributions of the source and target domains,and an adversarial mechanism is used to align the subdomain distributions between the two domains.Experimental validation on a publicly available variable-condition gearbox fault dataset demonstrates that the proposed method achieves an average recognition accuracy of over 96.25%.A comparison with other diagnostic methods further validates the effectiveness and superiority of the proposed approach.

关 键 词：判别损失项 卷积注意力模块 域对抗迁移网络 迁移学习 故障诊断 

分 类 号：TH17[机械工程—机械制造及自动化] TN0[电子电信—物理电子学]