摘要
在强背景噪声干扰下,快速峭度图提取滚动轴承微弱信号故障的特征效果并不明显。将迭代滤波(Iterative Filtering,IF)和快速峭度图相结合用于滚动轴承的微弱故障特征提取。滚动轴承故障振动信号通过迭代滤波进行自适应分解得到一组内禀模态分量,用迭代滤波对强噪声滚动轴承信号进行降噪处理,用快速峭度图构造最优带通滤波器,将滤波后信号的包络谱与轴承故障特征频率进行比较,从而诊断出具体故障。通过仿真和试验验证了所述方法的有效性及优点。
The effect of fast kurtogram to extract rolling bearings ’ weak fault signals is not obvious under interference of strong background noise. Here,rolling bearings’ weak fault feature extraction was conducted with the combination of iterative filtering and fast kurtogram. The faulty bearing’s vibration signals were adaptively decomposed into a group of intrinsic mode components with iterative filtering. The iterative filtering method was used to denoise rolling bearing’s vibration signals with strong noise. An optimal band-pass filter was constructed using the fast kurtogram.Finally,the envelope spectra of the filtered signals were compared with fault feature frequencies of rolling bearings to judge the diagnosed bearing’s fault types. The effectiveness and advantages of the proposed method were verified with numerical simulation and tests.
作者
钟先友
田红亮
赵春华
陈保家
陈法法
ZHONG Xianyou;TIAN Hongliang;ZHAO Chunhua;CHEN Baojia;CHEN Fafa(Hubei Key Laboratory of Hydroelectric Machinery Design ＆ Maintenance,Three Gorges University,orges University,Yichang 443002, Chin)
出处
《振动与冲击》
EI
CSCD
北大核心
2018年第9期190-195,共6页
Journal of Vibration and Shock
基金
宜昌市自然基础科学项目(A15-302-A02)
国家自然科学基金项目(51405264)
三峡大学人才启动基金(KJ2014B040)
关键词
迭代滤波
快速峭度图
经验模态分解
轴承故障诊断
iterative filtering
fast kurtogram
empirical mode decomposition（EMD）
bearing fault diagnosis
作者简介
钟先友 男,博士,副教授,1977年生
