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Fault Diagnosis of Rolling Bearing Based on Modified Deep Metric Learning Method
Shock and Vibration ( IF 1.6 ) Pub Date : 2021-02-27 , DOI: 10.1155/2021/6635008 Zengbing Xu 1, 2, 3 , Xiaojuan Li 1, 2 , Hui Lin 4 , Zhigang Wang 1, 2 , Tao Peng 5
Shock and Vibration ( IF 1.6 ) Pub Date : 2021-02-27 , DOI: 10.1155/2021/6635008 Zengbing Xu 1, 2, 3 , Xiaojuan Li 1, 2 , Hui Lin 4 , Zhigang Wang 1, 2 , Tao Peng 5
Affiliation
A novel fault diagnosis method of rolling bearing based on deep metric learning and Yu norm is proposed in this paper, which is called a deep metric learning method based on Yu norm (DMN-Yu). In order to solve the misclassification caused by the traditional deep metric learning based on distance metric function, a similarity criterion based on Yu norm is introduced into the traditional deep metric learning. Firstly, the deep metric learning neural network (DMN) is used to adaptively extract the fault feature parameters. Secondly, considering that the data samples at the boundary between different fault categories can be misclassified, the marginal Fisher analysis method based on Yu norm is used to optimize the features. And then, BPNN classifier of DMN-Yu method is used to fine tune the network parameters and diagnose the fault category. Finally, the effectiveness and feasibility of the proposed DMN-Yu method is verified with the rolling bearing fault diagnosis test. And the superiority of the proposed diagnosis method is validated by comparing its diagnosis accuracy with the deep metric learning method based on Euclidean distance (DMN-Euc), traditional deep belief network (DBN), and support vector machine (SVM) combined with the common time-domain statistical features.
中文翻译:
基于改进深度度量学习方法的滚动轴承故障诊断
提出了一种基于深度度量学习和余范数的滚动轴承故障诊断新方法,称为基于余准则的深度度量学习法(DMN-Yu)。为了解决传统的基于距离度量函数的深度度量学习引起的分类错误,在传统的深度度量学习中引入了基于Yu范数的相似性准则。首先,使用深度度量学习神经网络(DMN)自适应地提取故障特征参数。其次,考虑到不同断层类别之间边界的数据样本可能被错误分类,采用基于余范数的边际Fisher分析方法对特征进行了优化。然后,使用DMN-Yu方法的BPNN分类器对网络参数进行微调,并进行故障分类诊断。最后,通过滚动轴承故障诊断测试验证了所提出的DMN-Yu方法的有效性和可行性。通过与基于欧氏距离(DMN-Euc),传统深度信念网络(DBN)和支持向量机(SVM)的深度度量学习方法相比较,将诊断方法的诊断准确性进行了比较,从而验证了该诊断方法的优越性。时域统计功能。
更新日期:2021-02-28
中文翻译:
基于改进深度度量学习方法的滚动轴承故障诊断
提出了一种基于深度度量学习和余范数的滚动轴承故障诊断新方法,称为基于余准则的深度度量学习法(DMN-Yu)。为了解决传统的基于距离度量函数的深度度量学习引起的分类错误,在传统的深度度量学习中引入了基于Yu范数的相似性准则。首先,使用深度度量学习神经网络(DMN)自适应地提取故障特征参数。其次,考虑到不同断层类别之间边界的数据样本可能被错误分类,采用基于余范数的边际Fisher分析方法对特征进行了优化。然后,使用DMN-Yu方法的BPNN分类器对网络参数进行微调,并进行故障分类诊断。最后,通过滚动轴承故障诊断测试验证了所提出的DMN-Yu方法的有效性和可行性。通过与基于欧氏距离(DMN-Euc),传统深度信念网络(DBN)和支持向量机(SVM)的深度度量学习方法相比较,将诊断方法的诊断准确性进行了比较,从而验证了该诊断方法的优越性。时域统计功能。