摘要
针对高速动车组运行工况复杂、轴箱轴承故障率较高、背底噪声强和故障识别难度大的情况,提出基于改进变分模态分解(VMD)的动车组轴箱轴承故障识别方法。首先,运用能量差法和合成谱峭度法计算最优的变分模态分解关键参数;其次,基于相关系数、谱峭度及奇异值构建的评价参数,选取用于重构故障信号的本征模态分量;最后,对重构后的信号进行傅里叶变换,实现在强背底噪声情况下的故障特征频率识别,并通过模拟数据和真实动车组轴箱轴承试验数据对提出的方法进行验证。结果表明:提出的方法能够有效地在强背底噪声情况下重构带有预设的40或200 Hz故障特征频率的信号,重构后的信号最大程度保留了轴承的故障信息;故障特征频率识别效果好,能够为保障高速动车组的安全运行提供技术支撑。
Aiming at the issues of complex operating conditions of high-speed EMU trains,the high failure rate of axle box bearings,strong background noise and the difficulty of fault identification,a fault identification method of axle box bearings of EMUs based on improved variational modal decomposition(VMD)is proposed.Firstly,the key parameters of the optimal VMD are calculated by using methods of energy difference and synthetic spectral kurtosis.Secondly,based on the evaluation parameters constructed by correlation coefficients,spectral kurtosis and singular values,the intrinsic mode components used to reconstruct the fault signal are selected.Finally,Fourier transform is applied to the reconstructed signal to realize the recognition of the fault characteristic frequency under strong background noise;and the proposed method is verified by the simulated data and the experimental data of the axle box bearings of the real EMUs.The results show that the proposed method can effectively reconstruct the preset signal with a 40 or 200 Hz fault characteristic frequency under strong background noise,and the reconstructed signal retains the fault information of the bearings to the greatest extent.The proposed method proves a good recognition effect of fault characteristic frequency,which can provide technical support for ensuring the safe operation of high-speed EMUs.
作者
宋宏智
李秀杰
邱战国
邢群雁
杨兴宽
刘晏伊
SONG Hongzhi;LI Xiujie;QIU Zhanguo;XING Qunyan;YANG Xingkuan;LIU Yanyi(Single&Communication Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;Metals&Chemistry Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China)
出处
《中国铁道科学》
EI
CAS
CSCD
北大核心
2023年第3期146-154,共9页
China Railway Science
基金
北京市自然科学基金资助项目(L221016)
中国铁道科学研究院集团有限公司院基金课题(2022YJ136)。
关键词
高速动车组
轴箱轴承
故障识别
变分模态分解
VMD
High speed EMUs
Axle box bearing
Fault identification
Variational modal decomposition
VMD
