摘要
针对电机滚动轴承故障特征提取问题,提出了一种基于局部特征尺度分解(Local Characteristic Decomposition,LCD)的全矢包络谱(Full Vector Envelope Spectrum,FVES)技术。该方法通过正交采样技术获取两个互相垂直方向的振动信号,然后用LCD分别将其分别分解成系列内禀模态分量(Intrinsic Scale Component,ISC)之和;其次,用Hilbert变换对两个方向的ISC进行包络解调得到包络信号;最后运用FVES融合两个方向上的包络信号得到全矢包络谱。电机滚动轴承外圈故障分析结果表明,较之单一方向上信号的包络谱,全矢包络谱的频谱结构更为清晰、幅值更大。
In order to diagnose AM-FM class of motor failure, a full vector envelope spectrum technology （FVES） based on local characteristic decomposition （LCD） was proposed. Two directions of vibration signals were got by orthogoual sampling. Then, the signals were decomposed by LCD respectively to obtain intrinsic mode components of the series （ISC）. Secondly, envelope signals of ISCs were obtained by Hilbert Transform. Full vector envelope spectrum was ob- tained by vector spectrum technology. Rolling bearing outer ring failure analysis results show that full vector spectrum enve- lope spectrum structure and amplitude is clearer and greater, compared with a single direction signal envelope spectrum.
出处
《微特电机》
北大核心
2017年第7期30-33,共4页
Small & Special Electrical Machines
基金
河南省创新型科技人才队伍建设工程项目(C20150034)
河南省基础与前沿技术研究计划项目(162300410042)
河南省科技攻关项目(172102210116)
郑州工程技术学院科技创新团队建设计划项目(CXTD2017K1)
关键词
局部特征尺度分解
电机故障
全矢包络谱
内禀模态分量
local characteristic decomposition （LCD）
motor failure
full vector envelope spectrum
intrinsic scale component
