基于改进VMD和Teager能量算子解调的轴箱轴承故障诊断方法
黄梓幸1,宋冬利*,1,董俭雄1,田光荣2
(1.西南交通大学 牵引动力国家重点实验室,四川 成都 610031;2.中国铁道科学研究院集团有限公司 机车车辆研究所,北京 100081)
摘要:现行的高速列车轴箱轴承多采用温度监测和振动监测。针对高速列车轴箱轴承运行工况复杂、轴箱轴承振动信号故障特征难以提取的问题,提出改进VMD和Teager能量算子解调结合的故障诊断方法。该方法首先利用局域均值分解(LMD)的自适应分解性将轴承故障振动信号分解为多个PF分量,再通过构造融合冲击指标筛选有效的PF分量,有效分量被用于重构信号和确定VMD的模态参数K,最后选取VMD分解后信息熵最小值所在的IMF进行Teager能量算子解调分析,提取故障特征频。通过高速列车轴箱轴承专用试验台验证了该方法的有效性和优越性。结果表明,改进的VMD方法能有效克服垂向激励、环境噪声、共振等影响因素,提取出微弱的轴箱轴承早期故障特征。
关键词:轴箱轴承;故障诊断;变分模态分解;特征频
中图分类号:TH133.33;TN911.7 文献标志码:A doi:10.3969/j.issn.1006-0316.2022.12.007
文章编号:1006-0316 (2022) 12-0039-09
Fault Diagnosis Method of Axle Box Bearing Based on Improved VMD and Teager Energy Operator Demodulation
HUANG Zixing1,SONG Dongli1,DONG Jianxiong1,TIAN Guangrong2
( 1.State Key Laboratory of Traction Power , Southwest Jiaotong University, Chengdu 610031, China; 2.Locomotive & Car Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China )
Abstract:Temperature monitoring and vibration monitoring are commonly used for current high-speed train axle box bearings. A fault diagnosis method based on improved VMD and Teager energy operator demodulation was proposed  based on the complex operating conditions of high-speed train axle box bearings and the difficulty in extracting fault features of axle box bearing vibration signals,. We first decompose the bearing fault vibration signal into multiple PF components by using the adaptive decomposition of local mean decomposition (LMD). Then we filter out the effective PF components by constructing the fusion shock index. The effective components are used to reconstruct the signal and determine the VMD. Finally, we select the IMF where the minimum information entropy after VMD decomposition is located to carry out Teager energy operator demodulation analysis, and extract the fault characteristic frequency. The effectiveness and superiority of the method are verified by a special test bench for axle box bearings of high-speed trains. The results show that the improved VMD method can effectively overcome the vertical excitation, environmental noise, resonance and other influencing factors to extract weak early fault features of axle box bearings.
Key words:axle box bearing;fault diagnosis;variational modal decomposition;characteristic frequency
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收稿日期:2022-03-25
基金项目:国铁集团科研计划项目(J2020J006)
作者简介:黄梓幸(1997-),女,湖南长沙人,硕士研究生,主要研究方向为轴承动力学及故障诊断,E-mail:646663884@qq.com。*通讯作者:宋冬利(1971-),女,贵州沿河人,博士,高级实验师,主要从事列车综合安全评估与维修决策工作,E-mail:sdl.cds@163.com。
 

 

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