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A Bearing Fault Diagnosis Method with Unsupervised Deep Adaptive Network

Qing YangBaocai CuiHui XueDongsheng Wu

School of Automation and Electrical Engineering,Shenyang Ligong University

摘要:To improve the ability of the predictive model to generalize unlabeled data in fault diagnosis,an improved bearing fault diagnosis method with unsupervised deep adaptive network(UDAN) based on second-order statistics is presented.First,the motor vibration signal is transformed into a two-dimensional gray image to improve the extraction of transfer features.Then,the second-order statistics alignment of source domain and target domain is used to minimize the difference in domain distribution in the deep residual network.Finally,the combined loss function is constructed to realize the end to end adaptive fault diagnosis.Compared to other methods of unsupervised learning,experimental results show that UDAN fault diagnosis method has better generalization ability.
会议名称:

第33届中国控制与决策会议

会议时间:

2021-05-22

会议地点:

中国云南昆明

  • 专辑:

    工程科技Ⅱ辑; 信息科技

  • 专题:

    机械工业; 自动化技术; 自动化技术

  • DOI:

    10.26914/c.cnkihy.2021.023164

  • 分类号:

    TH133.3;TP18;TP277

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