【作者】 柳亦兵； 周雁冰； 辛卫东； 高青风； 何缨；

【Author】 LIU Yibing,ZHOU Yanbing,XIN Weidong,GAO Qingfeng,HE Ying School of Energy,Power and mechanical Engineering,North China Electric Power University,Beijing 102206,P.R.China

【机构】 华北电力大学能源动力与机械工程学院；

【摘要】 本文以某种型号的大型直驱风电机组为对象,对其主轴后轴承发生故障时的振动信号进行实测分析,主要探讨在强干扰环境和微弱故障条件下合适的故障特征提取和识别方法,采用振动信号的高阶统计特性区分正常状态和故障状态,以振动信号双谱的非高斯性强度作为特征值,经过主分量分析(PCA)对多维特征数据进行压缩降维,得到区分度较好的低维主分量特征值。对故障机组与正常机组的分析计算表明,双谱对风电机组轴承故障十分敏感,非高斯性强度特征值可以很好的反应故障与正常机组的差别,经过PCA特征压缩后得到的低维主分量具有较好的故障区分度。更多还原

【Abstract】 Vibration measurements on a large direct-drive wind turbine are carried out to detect the fault of the rolling element bearing on the rear part of the main shaft.The main purpose of this paper is to find an appropriate method for weak fault feature extraction and fault recognition from vibration signals under strong interference.We propose to use higher order statistics characteristics of vibration signals for differentiating normal condition from fault condition.Firstly the non-Gaussian intensity in different area in vibration signal bispectrum are extracted as the feature values,these multi-dimensional features are compressed by means of principal component analysis(PCA) to obtain some lower dimensional principal component features with better discrimination for different running condition.Analysis results show that the proposed method of feature extraction with the non-Gaussian intensity characteristic of the bispectrum is very sensitive to differentiate the normal running condition from failure ones and very clear to identify the bearing fault of wind turbine.更多还原