摘要
故障特征精确提取是实现轴承故障诊断的重要环节。卷积稀疏表示能够刻画特征的移位不变特性,非常适用于滚动轴承故障特征提取。然而,卷积稀疏表示忽略了故障冲击特征的周期性及不同尺度下的信号特性差异,制约了其在谐波成分和背景噪声等干扰下的特征提取能力。因此,提出了加权多尺度卷积稀疏表示用于分离振动信号中的周期性故障冲击特征,从而实现轴承故障诊断。在构建的稀疏表示模型中,利用多尺度变换将原始信号转换到不同尺度下,并在不同尺度下采用不同权重系数以达到抑制谐波成分等干扰的目的。同时,为了凸显故障冲击特征,建立了约束故障特征稀疏系数周期性的正则项,提高冲击特征分离能力。此外,引入交替方向乘子法和受控极小化方法推导出迭代求解算法。最后,通过分析提取特征的波形、包络谱及两种故障信息定量评估指标,验证了提出方法拥有优异的轴承复合故障冲击特征提取和诊断能力。
Accurate fault feature extraction is an important part of achieving bearing fault diagnosis.The convolutional sparse representation can characterize the shift-invariant property of features,which is very suitable for rolling bearing fault feature extraction.However,the convolutional sparse representation ignores the periodicity of fault impulse features and the difference of signal characteristics at different scales,which restricts its feature extraction ability under the interference of harmonic components and background noise.Therefore,a weighted multiscale convolutional sparse representation is proposed for separating the periodic fault impulse features in vibration signals to achieve bearing fault diagnosis.Specifically,in the constructed sparse representation model,the original signal is converted to different scales using multiscale transformation,and different weights are utilized in different scales to suppress the interferences such as harmonic components.Meanwhile,to promote fault impulse features,a regularization term that constrains the periodicity of the sparse coefficient of fault features is established to improve fault feature separation ability.In addition,the alternating direction method of multipliers and the majorization-minimization method are introduced to derive an iterative solving algorithm.Finally,by analyzing the waveform and envelope spectrum of extracted features and two quantitative evaluation indicators of fault information,the excellent capability of the proposed method in fault feature extraction and diagnosis of bearing compound faults is verified.
作者
王爽
丁传仓
曹懿
王报祥
江星星
Wang Shuang;Ding Chuancang;Cao Yi;Wang Baoxiang;Jiang Xingxing(Institute of Intelligent Manufacturing and Smart Transportation,Suzhou City University,Suzhou 215104,China;School of Rail Transportation,Soochow University,Suzhou 215131,China;School of Mechanical Engineering,Suzhou University of Science and Technology,Suzhou 215009,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2024年第5期197-207,共11页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(52205119)
江苏省自然科学基金(BK20220497)
中国博士后科学基金(2023M732535)项目资助。
关键词
加权多尺度卷积稀疏表示
滚动轴承
故障诊断
故障特征提取
weighted multiscale convolutional sparse representation
rolling bearing
fault diagnosis
fault feature extraction
