有限变工况特征迁移学习方法及其在高速列车轴箱轴承故障诊断中的应用

A transfer learning method for bearing fault diagnosis under finite variable working conditions and its application in train axle-box bearings fault diagnosis

以高速列车轴箱轴承为研究对象,提出了一种适用于有限数量变工况下的轴承故障诊断方法。该方法以有监督的学习模式构造自编码器,将不同工况下特征值集向参考工况下特征集做映射迁移,从而减弱由工况变化引起的轴承故障特征值改变的影响。再将迁移后的特征集输入由参考工况特征集预训练的基于卷积神经网络的故障诊断模型,实现变工况下轴承故障的诊断。凯斯西储大学轴承公开数据集和高速列车轴箱轴承数据集的试验结果表明,经监督式自编码器特征迁移后的轴承故障识别准确率有了较大提升,该方法能够较好的实现有限工况下的特征序列的迁移,解决工况变化带来的故障特征的畸变问题。

This article takes the high-speed train axle box bearing as the research object.A bearing fault diagnosis method is proposed to deal with finite variable working conditions,which is based on the supervised auto encoder feature representation transfer.The feature sequences of different working conditions are mapped to the reference condition feature sequences.In this way,the influence of condition change on bearing fault feature is decreased.The migrated features are inputted into the fault diagnosis model based on the convolution neural network,which is pre-trained by the reference condition training feature sets.Then,the axle box bearing fault diagnosis is achieved under variable working conditions.The open bearing data of Case Western Reserve University and the high-speed axle box bearing data are utilized.Experimental results show that the accuracy of fault identification has been greatly improved after feature migration.The method can achieve the feature migration under different working conditions and reduce the distortion of fault features caused by the change of working conditions.