基于chirplet path tracing-VMD的变转速信号故障特征提取  

Fault feature extraction method for variable speed signal based on chirplet path tracing-VMD

作　　者：周福成[1] 白怡凡 付滋翔 何玉灵[1] 唐贵基[1] Zhou Fucheng;Bai Yifan;Fu Zixiang;He Yuling;Tang Guiji(Department of Mechanical Engineering,and also the Hebei Key Laboratory of Electric Machinery Health Maintenance and Failure Prevention,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学机械工程系暨河北省电力机械装备健康维护与失效预防重点实验室,保定071003

出　　处：《国外电子测量技术》2023年第9期193-198,共6页Foreign Electronic Measurement Technology

基　　金：国家自然科学基金(52177042);河北省自然科学基金(E2020502031);中央高校基本科研业务费专项资金(9160423012)项目资助。

摘　　要：针对变分模态分解方法在提取故障特征时处理非平稳信号能力不足的情况,提出基于线调频小波路径追踪(chirplet path tracing)的自适应改进变分模态分解(variational mode decomposition,VMD)方法。该方法首先利用线调频小波路径追踪算法由非平稳信号得到估计转速变化曲线;其次对目标信号进行等角度重采样,通过转速变化曲线将信号从时域转化为角域;再次采用奇异值分解确定角域信号的降噪阶次,当该阶次自适应变分模态分解的分量个数后,对重采样信号进行变分模态分解处理;最后对分解后的各信号分量进行Hilbert包络谱分析,从而提取故障特征。通过对齿轮箱轴承故障进行仿真和实验验证,表明该方法能有效提取变转速信号的故障特征。To address the situation that the variational modal decomposition method(VMD) is not capable of handling non-smooth signals when extracting fault features,adaptive improved variational modal decomposition method based on chirplet path tracing is proposed.Firstly,estimate its speed variation curve from the non-smooth signal by chirplet path tracing.Then the curve is used to resample the target signal at equal angles,that is,to convert the time domain signal into the frequency domain signal.Next using the singular value decomposition determines the noise reduction order of the signal in the angular domain.And when this order is adaptive to the number of components of the VMD,the resampled signal is processed for the variational modal decomposition.Finally,to extract the fault features,Hilbert envelope spectrum analysis is performed on each signal component after decomposition.Through simulation and experimental signal analysis,it is shown that the method can effectively extract the fault characteristics of the variable speed signal.

关 键 词：线调频小波路径追踪 变分模态分解 变转速信号 故障特征提取 

分 类 号：TH133.33[机械工程—机械制造及自动化]