摘要
为了提高域自适应故障诊断方法在无标签目标域上的泛化能力,提出发掘振动信号中潜在因果关系的故障诊断模型.基于序列变分自编码器(VAE),利用故障标签学习与故障类型相关的稳健特征表示.在域标签的引导下分离出与故障无关但反映数据域分布特性的因素,逐步将振动信号进行因果解耦.重新构建序列VAE的证据下界(ELBO),以有效引导解耦过程.结合域自适应技术,拉近源域和目标域共有故障特征在特征空间中的距离,提高特征编码器所学特征的泛化性能.在CWRU和IMS数据集上的实验结果表明,因果网络的特征解耦能力可与域自适应方法互为补充,增强故障诊断模型在域自适应任务中的性能.
To improve the generalization capability of domain adaptive fault diagnosis methods on unlabeled target domains,a fault diagnosis model to uncover the latent causal relationships within vibration signals was proposed.Based on a sequential variational autoencoder(VAE),robust feature representations associated with fault types were learned using fault labels.Guided by the principles of domain labels,factors unrelated to the faults yet reflective of the domain’s data distribution were separated,thereby progressively decoupling the vibration signals causally.The evidence lower bound(ELBO)of the sequential VAE was restructured to effectively guide the decoupling process.By incorporating domain adaptation techniques,the shared fault features’distance between the source and target domains was brought close in the feature space,enhancing the generalization of the features learned by the encoder.Experimental results on the CWRU and IMS datasets show that the feature decoupling capability of the causal network complements domain adaptation methods,improving the performance of the fault diagnosis model in domain adaptation tasks.
作者
黄爱颖
李晓辉
孙淑娴
朱逸群
HUANG Aiying;LI Xiaohui;SUN Shuxian;ZHU Yiqun(Marketing Service Center,State Grid Tianjin Electric Power Company,Tianjin 300160,China)
出处
《浙江大学学报(工学版)》
北大核心
2025年第7期1523-1531,共9页
Journal of Zhejiang University(Engineering Science)
基金
营服-研发2023-03。
关键词
滚动轴承
因果解耦
变分自编码器(VAE)
域自适应
故障诊断
rolling bearing
causal disentanglement
variational autoencoder(VAE)
domain adaptation
fault diagnosis
