摘要
风电机组轴承处于早期故障阶段时,故障特征信号微弱,受环境噪声及信号衰减的影响较大,因此轴承早期故障特征的提取一直是个难点。为了有效提取风机滚动轴承的故障特征,提出了基于变分模态分解(Variational Mode Decomposition,VMD)和奇异值能量差分谱的特征提取方法。首先对轴承信号进行VMD分解得到一系列固有模态函数(Intrinsic Mode Function,IMF),然后选取敏感IMF进行奇异值分解,并利用奇异值能量差分谱选取有效奇异值进行信号重构,最后对重构信号进行包络谱分析,进而提取故障特征。实验分析结果验证了所述方法的有效性。
In early failure period of wind turbine bearing,the fault feature signal is weak and affected by environmen- tal noise and signal attenuation seriously, so it is difficult to extract the fault feature. In order to effectively extract the fault characteristics of rolling bearings, a feature extraction method based on variational mode decomposition （VMD） and singular value energy difference spectrum is proposed in this paper. Firstly, a series of intrinsic mode functions were obtained by processing the bearing signal with VMD algorithm. Then the sensitive IMF was selected to be dis- posed by singular value decomposition. By using the singular value energy difference spectrum, the effective singular value is selected to reconstruct the signal. At last, the fault feature can be extracted by analyzing envelope spectrum of the reconstructed signal. In the end,the effectiveness of the proposed method is verified through experiment.
作者
张伟
白恺
宋鹏
杨伟新
赵洪山
王正宇
Zhang Wei Bai Kai Song Peng Yang Weixin Zhao Hongshan Wang Zhengyuz(School of Electrical and Electronic Engineering,North China Electric Power University,Baoding 072003,China State Grid Jibei Electric Power Co. Ltd. Research Institute,North China Electric Power Research Institute Co. Ltd., Beijing 100045, China)
出处
《华北电力技术》
CAS
2017年第3期59-64,共6页
North China Electric Power
基金
国家科技支撑计划(2015BAA06B03)资助
关键词
滚动轴承
VMD
奇异值能量差分谱
信号重构
rolling bearing, VMD, singular value energy difference spectrum, signal reconstruction
