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Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition
Shock and Vibration ( IF 1.2 ) Pub Date : 2020-09-01 , DOI: 10.1155/2020/8582732
Shiyuan Liu 1, 2 , Xiao Yu 3 , Xu Qian 2 , Fei Dong 3
Affiliation  

In real industrial scenarios, the working conditions of bearings are variable, and it is therefore difficult for data-driven diagnosis methods based on conventional machine-learning techniques to guarantee the desirable performance of diagnosis models, as the models assume that the distributions of both the training and testing data are the same. To enhance the performance of the fault diagnosis of bearings under different working conditions, a novel diagnosis framework inspired by feature extraction, transfer learning (TL), and feature dimensionality reduction is proposed in this work, and dual-tree complex wavelet packet transform (DTCWPT) is used for signal processing. Additionally, transferable sensitive feature selection by ReliefF and the sum of mean deviation (TSFSR) is proposed to reduce the redundant information of the original feature set, to select sensitive features for fault diagnosis, and to reduce the difference between the marginal distributions of the training and testing feature sets. Furthermore, a modified feature reduction method, the local maximum margin criterion (LMMC), is proposed to acquire low-dimensional mapping for high-dimensional feature spaces. Finally, bearing vibration signals collected from two test rigs are analyzed to demonstrate the adaptability, effectiveness, and practicability of the proposed diagnosis framework. The experimental results show that the proposed method can achieve high diagnosis accuracy and has significant potential benefits in industrial applications.

中文翻译:

变工况下基于敏感特征传递学习和局部最大极限准则的滚动轴承故障诊断

在实际的工业场景中,轴承的工作条件是可变的,因此基于常规机器学习技术的数据驱动诊断方法很难保证诊断模型具有理想的性能,因为这些模型假定两种模型的分布都是相同的。培训和测试数据相同。为了提高轴承在不同工况下的故障诊断性能,提出了一种基于特征提取,传递学习(TL),特征维数减少的新型诊断框架,以及双树复小波包变换(DTCWPT)。 )用于信号处理。另外,提出了利用ReliefF和均值偏差之和(TSFSR)进行可转移的敏感特征选择,以减少原始特征集的冗余信息,选择用于故障诊断的敏感特征,并减少训练和测试的边际分布之间的差异。功能集。此外,提出了一种改进的特征约简方法,即局部最大余量准则(LMMC),以获取高维特征空间的低维映射。最后,分析了从两个试验台收集的轴承振动信号,以证明所提出的诊断框架的适应性,有效性和实用性。实验结果表明,该方法具有较高的诊断精度,在工业应用中具有明显的潜在优势。选择敏感特征进行故障诊断,并减少训练和测试特征集的边际分布之间的差异。此外,提出了一种改进的特征约简方法,即局部最大余量准则(LMMC),以获取高维特征空间的低维映射。最后,分析了从两个试验台收集的轴承振动信号,以证明所提出的诊断框架的适应性,有效性和实用性。实验结果表明,该方法具有较高的诊断精度,在工业应用中具有明显的潜在优势。选择敏感特征进行故障诊断,并减少训练和测试特征集的边际分布之间的差异。此外,提出了一种改进的特征约简方法,即局部最大余量准则(LMMC),以获取高维特征空间的低维映射。最后,分析了从两个试验台收集的轴承振动信号,以证明所提出的诊断框架的适应性,有效性和实用性。实验结果表明,该方法具有较高的诊断精度,在工业应用中具有明显的潜在优势。为了获得高维特征空间的低维映射,提出了局部最大余量准则(LMMC)。最后,分析了从两个试验台收集的轴承振动信号,以证明所提出的诊断框架的适应性,有效性和实用性。实验结果表明,该方法具有较高的诊断精度,在工业应用中具有明显的潜在优势。提出了局部最大余量准则(LMMC),以获取高维特征空间的低维映射。最后,分析了从两个试验台收集的轴承振动信号,以证明所提出的诊断框架的适应性,有效性和实用性。实验结果表明,该方法具有较高的诊断精度,在工业应用中具有明显的潜在优势。
更新日期:2020-09-01
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