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

作　　者：盛嘉玖 陈果[2] 康玉祥 贺志远 王浩 尉询楷[3] 刘传宇 SHENG Jiajiu;CHEN Guo;KANG Yuxiang;HE Zhiyuan;WANG Hao;WEI Xunkai;LIU Chuanyu(College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;College of General Aviation and Flight,Nanjing University of Aeronautics and Astronautics,Liyang Jiangsu 213300,China;Beijing Aeronautical Engineering Technical Research Center,Beijing 100076,China;Unit 92281 of People’s Liberation Army of China,Zhucheng Shandong 262200,China)

机构地区：[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

出　　处：《航空动力学报》2024年第9期364-376,共13页Journal of Aerospace Power

基　　金：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)。

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%。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 

分 类 号：V263.6[航空宇航科学与技术—航空宇航制造工程]