改进的多尺度形态学大型风力发电机轴承故障研究  

RESEARCH ON BEARING FAILURES OF LARGE-SCALE WIND TURBINES WITH IMPROVED MULTI-SCALE MORPHOLOGY

作　　者：陈璐 高文英[2] CHEN Lu;GAO WenYing(Department of Basic Science,Jilin University of Architecture and Technology,Changchun 130114,China;Institute of Technology,Shenyang Polytechnic College,Shenyang 110045,China)

机构地区：[1]吉林建筑科技学院基础科学部,长春130114 [2]沈阳职业技术学院科技学院,沈阳110045

出　　处：《机械强度》2021年第2期268-274,共7页Journal of Mechanical Strength

基　　金：辽宁省教育厅科学研究经费项目(LSZZR2010)资助。

摘　　要：大型风力发电机轴承的故障冲击信号通常受到复杂载荷和强背景噪声的干扰,其早期故障不易检测。针对这个问题,基于信息熵(Information Entropy,I_(E))和特征能量因子(Feature Energy Factor,F_(EF))提出一种改进的多尺度形态学分析方法。形态梯度乘积算子(Morphology Gradient Product Operation,MGPO)是一种有效的提取滚动轴承冲击信号的形态学算子,为了能够提取更为详细的故障特征信息,基于MGPO算子提出了多尺度形态学分析方法。为了改进峭度准则和信噪比在选择最优尺度上的不足,基于信息熵和特征能量因子提出一种综合的尺度范围选则方法,试验结果表明提出的算法具有一定的优越性。The fault impact signal of bearing on large-scale wind turbine is usually disturbed by complex loads and strong background noise,and its incipient faults are not easy to detect.To solve this problem,in this paper,an improved multi-scale morphological analysis method based on information entropy(I_(E))and feature energy factor(F_(EF))is proposed.Morphology gradient product operation(MGPO)is an effective morphology operator for extracting rolling bearing impact signals.This paper proposes a multi-scale morphological analysis method based on MGPO operator in order to extract more detailed fault feature information,and for improving the inadequacy of the kurtosis criterion and the signal-to-noise ratio in selecting the optimal scale,in this paper,a comprehensive scale range selection method based on information entropy and characteristic energy factor is also proposed.Experimental and comparative results show that the algorithm proposed in this paper has a certain of advantages.

关 键 词：风力发电机 多尺度形态学 信息熵 特征能量因子 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化]