摘要
针对传统无监督领域自适应方法扩展到多工况滚动轴承故障诊断场景适用性较弱的问题,提出了一种多源域自适应残差网络(multi-source domain adaptive residual network,MDARN),通过对齐来自多个源域的相关子域,从而提高模型在多工况下的故障诊断性能。首先,利用ResNeXt残差网络从源域和目标域充分提取可迁移特征;然后,引入局部最大平均差异(local maximum mean difference,LMMD)准则,以两个源域的子域为基础对齐目标域中相关子域,减少相关子域间和全局域间的分布差异;最后,利用美国凯斯西储大学轴承数据集和MFS机械综合故障试验台产生的真实的轴承振动数据集,对所提方法进行了试验验证。结果表明,该方法在多工况下的平均故障诊断精度高达99.76%。与现有代表性方法相比,所提方法具有更好的故障诊断效果。
Here,aiming at the problem of weaker applicability of traditional unsupervised domain adaptation methods in multi-working condition rolling bearing fault diagnosis scenarios,a multi-source domain adaptative residual network(MDARN)was proposed.By aligning relevant subdomains from multiple source domains,MDARN could improve its fault diagnosis performance under multiple working conditions.Firstly,ResNeXt residual network was used to fully extract transferable features from source domain and target domain.Then,the local maximum mean difference(LMMD)criterion was introduced to align relevant subdomains in target domain based on subdomains of two source domains to reduce distribution differences among relevant subdomains and global domain.Finally,the proposed method was experimentally verified using the bearing dataset of Case Western Reserve University,US and the actual bearing vibration dataset generated by MFS mechanical comprehensive fault test bench.The results showed that the average fault diagnosis accuracy of this method under multi-working condition reaches 99.76%;compared with existing representative methods,the proposed method has better fault diagnosis effect.
作者
高学金
张震华
高慧慧
齐咏生
GAO Xuejin;ZHANG Zhenhua;GAO Huihui;QI Yongsheng(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;MOE Engineering Research Center of Digital Community,Beijing 100124,China;Beijing Lab for Urban Rail Transit,Beijing 100124,China;Beijing Municipal Key Lab of Computational Intelligence and Intelligent System,Beijing 100124,China;School of Electric Power,Inner Mongolia University of Technology,Hohhot 010051,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2024年第7期290-299,共10页
Journal of Vibration and Shock
基金
北京市自然科学基金(4222041)。
