一种用于滚动轴承故障诊断的改进EWT方法
An improved EWT method for fault diagnosis of rolling bearings
盛嘉玖 1陈果 2康玉祥 1贺志远 1王浩 3尉询楷 3刘传宇4
作者信息
- 1. 南京航空航天大学民航学院,南京 211106
- 2. 南京航空航天大学通用航空与飞行学院,江苏溧阳 213300
- 3. 北京航空工程技术研究中心,北京 100076
- 4. 中国人民解放军92281部队,山东诸城 262200
- 折叠
摘要
针对经验小波变换(EWT)在滚动轴承故障信号最优频带提取中存在的问题,提出一种基于提取能量包络趋势线以自适应划分频带的改进EWT方法,并应用于滚动轴承故障诊断.利用Teager能量算子将频谱转换成能量谱,通过反复希尔伯特变换得到能量包络线.提取极大值并平滑处理,获得能量包络趋势线,对其进行1阶差分,选取有效极值点以自适应划分频带.构造一种归一化故障特征频率显著性指标,作为故障诊断和最优共振频带选取的有效判据.通过滚动轴承故障仿真和试验数据对算法进行验证.结果表明:相比于原始EWT,该方法可有效识别滚动轴承早期故障并合理选取最优共振频带.针对外、内圈故障数据所提指标可平均提升48.0％和174.1％.
Abstract
Considering the problem of empirical wavelet transform(EWT)in extracting optimal frequency band of the rolling bearing fault signal,an improved EWT method based on extracting energy envelope trend line to adaptively divide frequency band was proposed and applied to rolling bearing fault diagnosis.The Teager energy operator was used to convert the spectrum into energy spectrum,and the energy envelope was obtained by repeated Hilbert transform.Local maximum values were extracted and smoothed to obtain the energy envelope trend line,and the first-order difference was performed to select effective extreme points to adaptively divide the frequency band.A normalized fault characteristic frequency saliency index was constructed as an effective criterion for fault diagnosis and optimal resonance frequency band selection.The algorithm was verified by rolling bearing fault simulation and experiment data.The results showed that compared with the original EWT,the proposed method can effectively identify the early faults of rolling bearings and reasonably select the optimal resonance frequency band.The proposed indexes for the outer and inner race fault data can be increased by 48.0％and 174.1％on average.
关键词
滚动轴承/经验小波变换/故障诊断/共振解调/自适应信号分解Key words
rolling bearings/empirical wavelet transform/fault diagnosis/resonance demodulation/adaptive signal decomposition引用本文复制引用
基金项目
国家重大专项计划(J2019-Ⅳ-004-0071)
国家自然科学基金(52272436)
江苏省研究生科研与实践创新计划项目(KYCX20_0211)
出版年
2024
NETL
NSTL
万方数据