基于EMD-K-HT的风电机组滚动轴承故障特征提取方法研究  

Rolling Bearing of Wind Turbines Fault Feature Extraction Method Based on EMD-K-HT

作　　者：马鲁[1] 陈国初[1] 王海群[1] 

机构地区：[1]上海电机学院电气学院,上海200240

出　　处：《电力学报》2015年第2期105-110,148,共7页Journal of Electric Power

基　　金：上海市教委科研创新项目(13YZ140);上海市教委重点学科资助(J51901)

摘　　要：针对工程上滚动轴承实时监测和故障诊断的需要,对6205-2RS JEM SKF深沟球轴承进行研究,提出了基于EMD-K-HT的风电机组滚动轴承故障特征提取方法。利用EMD适用于非线性和非平稳信号的自适应分解,将其用于风电机组滚动轴承故障特征的提取。首先对于实测的滚动轴承振动信号进行EMD分解,然后选取峭度(K)高的特征模态函数进行Hilbert(HT)变换,最后利用获取的瞬时频率作为故障特征对滚动轴承进行故障诊断。实验结果表明,基于EMD-K-HT变换后可以获得滚动轴承故障信号的频率特征,从而有效的识别滚动轴承的故障类型。Due to the need of real-time monitoring and fault diagnosis of rolling bearing on the project, we put forward EMD-K-HT method based on 6205-2RS JEM SKF deep groove ball bearing study. Because the EMD is suitable for nonlinear and non-stationary signal adaptive decomposition, it is applied to rolling bearing fault feature extraction of wind turbines. Firstly the measured vibration signal of the rolling bearing in the EMD decomposition, and then select the IMF of high kurtosis（K） to Hilbert（HT） transform, finally the obtain instantaneous frequency as the fault characteristic for rolling bearing fault diagnosis. The experimental result indicates that the fault types of rolling bearing can he exactly identified by this method.

关 键 词：故障诊断 滚动轴承 EMD-K-HT变换 特征提取 

分 类 号：TM315[电气工程—电机] TH133[机械工程—机械制造及自动化]