基于改进SAE(堆叠自编码器)与温振融合的高速列车轴箱轴承轻微故障诊断方法
徐潇宋冬利王梓帆
Diagnosis Method for High-Speed Train Axle Box Bearing Slight Faults Based on Improved SAE and Temperature-Vibration Fusion
XU XiaoSONG DongliWANG Zifan
-
作者信息:西南交通大学轨道交通运载系统全国重点实验室， 610031， 成都
-
Affiliation:State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, 610031, Chengdu, China
-
关键词:
-
Key words:
-
DOI:10.16037/j.1007-869x.2025.04.041
-
中图分类号/CLCN:U270.331+2
-
栏目/Col:应用技术
摘要:
目的］高速列车轴箱轴承服役环境复杂多变，其单源信号对微弱故障的诊断精度不足。为了提高轴箱轴承早期微弱故障的诊断精度，有必要结合轴承温度、振动多源故障信息，研究一种温振特征融合驱动的高速列车轴箱轴承轻微故障诊断方法。［方法］首先，设计了一种AE(自编码器)驱动的轴承温度特征提取方法，以获取轴承异常温度特征，并采用EMD(经验模态分解)方法对振动信号进行处理，以获取有效振动IMF(本征模态函数)分量的统计特征。然后，通过优化基于SAE(堆叠自编码器)的降维算法，提出了一种温振特征有效融合方法，以实现温度特征与振动特征的非线性融合与降维。最终，结合BP(反向传播)神经网络，建立了基于温振特征融合的轴箱轴承轻微故障诊断模型。并利用高速列车滚动轴承试验台采集的数据对模型进行验证。［结果及结论］相较于基于单源信号特征的故障诊断方法，基于温振特征融合的诊断方法具有更高的故障诊断精度，平均诊断准确率可达到99%以上。相较于采用PCA(主成分分析)温振模型，采用所提的温振融合轴承诊断模型更准确有效。
Abstracts:
［Objective］ The operating environment of axle box bearings in high-speed train is complex and changeable, and the diagnostic accuracy of single-source signals for weak faults is insufficient. To improve the diagnostic accuracy of early weak fault of axle box bearing, it is necessary to study a diagnosis method for high-speed train axle box bearing slight fault driven by temperature-vibration feature fusion, in combination with the multi-source fault information of bearing temperature and vibration. ［Method］ First, an AE (auto encoder) driven bearing temperature feature extraction method is designed to obtain the abnormal bearing temperature features, and EMD (empirical modal decomposition) method is used to process the vibration signal，so as to obtain the statistical features of the effective vibration IMF (intrinsic modal function). Then, by optimizing the dimensionality reduction algorithm based on SAE (stacked auto encoder), an effective fusion method of temperature-vibration features is proposed to achieve nonlinear fusion and dimensionality reduction of temperature and vibration features. Finally, combined with BP (back propagation) neural network, a slight fault diagnosis model for axle box bearing based on temperature-vibration feature fusion is established. And the model is validated by the test data collected from the high-speed train rolling bearing test bench. ［Result & Conclusion］ Compared with single-source feature-driven fault diagnosis method, the fault diagnosis accuracy of the diagnosis method based on temperature-vibration feature fusion is higher, with an average diagnosis accuracy rate of over 99%. Compared with the PCA (principal component analysis) temperature-vibration model, the proposed temperature-vibration fusion bearing diagnosis model is more accurate and effective.
- 上一篇: 都市圈轨道交通调度指挥一体化模式研究
- 下一篇: 中低速磁浮接触轨动态检测系统设计与研究
过刊浏览
资料下载
友情链接