燕山大学学报2017，Vol.41Issue(5)：401-406,6.DOI:10.3969/j.issn.1007-791X.2017.05.004
小波包熵与多核学习在列车转向架轴承故障诊断中的应用
Application of wavelet packet entropy and multi kernel learning in fault diagnosis of train bogie bearing
摘要
Abstract
A new method based on the wavelet packet entropy and multiple kernel learning is proposed in order to improve the accuracy and efficiency of the fault diagnosis of train bogie bearing. First, the method of wavelet packet is used to decompose the rolling bearing vibration signals into three-layer, characteristic entropy is extracted, and then the eigenvector of wavelet packet of the vibrating signals is constructed.Second, a multi kernel learning is employed to learn a kernel function and the classifier from the training samples.Finally, the trained classifier is used to identify the fault type of the train bogie bearing.The results show that the method proposed can be used to accurately and effectively realize the fault diagnosis of train bearing, provid a good reference for the actual train bogie rolling fault diagnosis.关键词
列车转向架轴承/故障诊断/小波包熵/多核学习Key words
train bogie bearing/fault diagnosis/wavelet packet entropy/multi kernel learning分类
机械制造引用本文复制引用
周彭滔,单奇,叶运广..小波包熵与多核学习在列车转向架轴承故障诊断中的应用[J].燕山大学学报,2017,41(5):401-406,6.基金项目
国家自然科学基金资助项目(51475387) （51475387）
中央高校基本业务费专项基金项目(2682014CX033) （2682014CX033）
四川省科技创新苗子工程项目(2015102) （2015102）
