基于多源动态加权和改进域对抗网络的齿轮箱故障诊断
Gearbox Fault Diagnosis Based on Multi-source Dynamic Weighting and Improved Domain Adversarial Network
叶成帆 1战洪飞 1林颖俊 2余军合 1王瑞1
作者信息
- 1. 宁波大学 机械工程与力学学院,浙江宁波 315211
- 2. 中银(宁波)电池有限公司,浙江宁波 315040
- 折叠
摘要
现有的齿轮箱多源迁移诊断模型在不同迁移任务中的泛化能力有限,同时忽略了目标域和每个源域之间的特征分布差异对模型的影响.为了解决上述问题,提出了一种多源动态加权和改进域对抗网络的故障诊断方法.首先,通过改进一维卷积神经网络提取所有样本的高维特征;然后,构建混合核函数作为最大度量差异中的核函数,并设置具有可变参数的映射函数作为核函数权重,自适应度量域间差异;最后利用目标域和每个源域相应的度量差异和域对抗损失作为联合权重赋予域对抗策略.通过不同轴速下的齿轮箱数据集验证了该方法的泛化能力和有效性,为基于大数据的故障诊断的研究及应用提供参考.
Abstract
Existing gearbox multi-source migration diagnosis models have limited generalization capabilities in different migration tasks.In addition,the impact of feature distribution differences between the target domain and each source domain on the model is ignored.To solve these problems,a fault diagnosis method based on multi-source dynamically weighted and improved domain adversarial networks is proposed.First,high-dimensional features of all samples are extracted through one-dimensional convolutional neural network;then,the kernel function in the maximum metric difference is improved into an adaptive hybrid kernel function,and a mapping with learnable parameters is introduced.The function is used as a hybrid weight to adaptively measure the difference between domains;finally,the corresponding metric difference and domain adversarial loss of the target domain and each source domain are used as a joint weight to assign to the domain adversarial strategy.The data sets of gearboxes at different shaft speeds verify the generalization ability and effectiveness of this method across working conditions,providing a reference for the research and application of fault diagnosis based on big data.
关键词
故障诊断/多源迁移学习/跨工况/多源动态加权/改进域对抗网络Key words
fault diagnosis/multi-source transfer learning/cross-working conditions/multi-source dynamic weighting/improved domain adversarial network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据