基于改进连续小波变换增强非凸正则项稀疏分解的滚动轴承变转速故障冲击特征提取方法  

Fault Transients Extraction of Rolling Bearings under Varying Speed via Modified Continuous Wavelet Transform Enhanced Nonconvex Sparse Representation

作　　者：张春林 吴允恒 蔡克燊 冯亚东 万方义[1] 张安[1] ZHANG Chunlin;WU Yunheng;CAI Keshen;FENG Yadong;WAN Fangyi;ZHANG An(School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072;Nanjing Institute of Electronic Equipment,Nanjing 210007)

机构地区：[1]西北工业大学航空学院,西安710072 [2]南京电子设备研究所,南京210007

出　　处：《机械工程学报》2025年第1期172-186,共15页Journal of Mechanical Engineering

基　　金：国家自然科学基金(52105123);陕西省自然科学基金(2020JQ-127)资助项目。

摘　　要：针对变转速工况下滚动轴承非周期性故障冲击特征信号高保真提取问题,提出改进Morlet连续小波变换增强的非凸正则项稀疏分解方法。通过引入波形调节因子构造的改进Morlet小波基函数具有振荡属性可调的特性,能够匹配具有不同波形特征的故障冲击信号。将定转速下采用的包络谐噪比引入变转速工况,提出角度域包络谐噪比指标,实现对波形调节因子及阈值参数的优化。在此基础上,将改进Morlet连续小波变换与广义最小最大非凸正则项相结合形成稀疏分解模型;相较于离散小波变换,改进Morlet连续小波变换更容易将非周期性冲击型故障信号映射到时频稀疏域,进而通过稀疏模型求解实现非周期性故障冲击信号的提取。通过仿真信号及实验数据对该方法的有效性进行了验证,并与传统阈值降噪、频带滤波、基于品质因子可调小波稀疏分解等方法进行了比较。结果表明,所提方法能够有效提取出变转速工况下滚动轴承非周期性故障冲击特征信号。To extract the nonperiodic fault transients of rolling bearings under varying speed with high fidelity,a method termed nonconvex sparse representation enhanced by modified continuous Morlet wavelet transform is proposed.A waveform adjusting factor is introduced which enables the modified Morlet wavelet to well match the fault impulses with different oscillating properties,and an index termed angular envelope harmonic to noise ratio is developed based on which the waveform adjusting factor and threshold are optimized.The modified continuous Morlet wavelet transform enjoys higher coefficients sparisity in decomposing the vibration signal compared with discrete wavelet transforms.The sparse representation model is then fabricated via combining the modified continuous Morlet wavelet transform with the nonconvex penalty function,and nonperiodic fault transients are extracted via further solving the sparse model.The effectiveness of the proposed method is validated via analysing both simulation and experimental data,as well as compared with traditional thresholding denoising,frequency band filtering,and tunable Q-factor wavelet transform enhanced sparse representation methods.The results show that the proposed method could effectively extract the nonperiodic fault impulses of rolling bearings under time-varying speed with high fidelity.

关 键 词：轴承变转速工况 非周期性故障冲击 改进Morlet连续小波变换 非凸正则项稀疏分解 角度域包络谐噪比 

分 类 号：TH17[机械工程—机械制造及自动化] TH133