摘要
为有效提取城市轨道车辆牵引电机轴承的故障特征,提出一种基于本征模式分量(IMF)聚合与奇异值分解(SVD)相结合的轴承故障诊断方法。该方法首先运用经验模式分解(EMD)将原始振动信号分解成一系列本征模式分量;其次在利用皮尔逊积矩法进行本征模式分量的筛选后将剩余的分量聚合重构,再将重构信号运用奇异值分解降噪;最后对降噪信号进行Hilbert谱分析,实现轴承故障特征向量的提取。城市轨道车辆牵引电机轴承实测数据的分析结果表明该方法能够有效提取故障特征信号,对轴承故障进行有效的诊断。
A method of fault diagnosis based on aggregation of intrinsic mode functions （IMF） and singular val- ue decomposition （SVD） denosing is proposed to effectively extract the featurs of fault for rolling bearings of traction motor in the urban rail vehicles. In the method, the vibration signal is firsly decomposed into a series of IMF components, and some IMF components are selected to reconstruct a new signal according to the Pearson correlation coefficient. Then, the SVD technique is nally, Hilbert spectrum analysis is used to acquire mental results show that the proposed method can adopted to reduce the noise in the reconstructed signal. Fi- the bearing fault feature frequency information. The experi- extract the fault features correctly and recognize the rolling bearing faults effectively.
出处
《测控技术》
CSCD
2017年第1期14-17,22,共5页
Measurement & Control Technology
基金
上海市科学技术委员会科研计划(12210501200)
上海工程技术大学研究生科研创新专项项目(E3-0903-16-01087)
关键词
牵引电机
轴承
故障诊断
本征模式分量(IMF)
奇异值分解
traction motor
rolling bearing
fault diagnosis
intrinsic mode function （IMF）
singular value de- composition （SVD）
