摘要
特征提取在滚动轴承故障诊断中起着至关重要的作用,然而实测的振动信号本质上是复杂的、非平稳的,同时故障轴承的脉冲特征常常淹没于噪声中。为了有效提取强噪声背景下的滚动轴承故障信息,提出一种基于总变差去噪(Total Variation Denoising,TVD)和快速谱相关(Fast Spectral Correlation,Fast-SC)相结合即 TVD-Fast SC 故障特征提取方法;首先,利用总变差去噪方法对振动信号进行消噪,提高信号的信噪比(SNR);然后,对去噪后的信号进行快速谱相关分析,准确地识别出轴承的故障特征频率。仿真和实验结果表明,该方法可以有效地提取出滚动轴承的微弱故障特征信息,分析效果优于直接快速谱相关方法和小波阈值去噪与快速谱相关结合的方法,为滚动轴承微弱故障特征提取提供一种有效的方法。
Feature extraction plays a crucial role in rolling bearing fault diagnosis. However, vibration signals measured are complex and non-stationary inherently, and pulse features of faulty bearings are often submerged in noise. Here, in order to effectively extract the fault information of rolling bearings under strong background noise, a fault feature extraction method based on combination of total variation de-noising and fast spectral correlation(TVD-FSC) was proposed. Firstly, the total variation de-noising method was used to de-noise vibration signals and improve their signal-to-noise ratios. Then, the fast spectral correlation analysis was performed for de-noised signals to correctly identify fault feature frequencies of a bearing. Simulation and test results showed that the proposed method can be used to effectively extract the weak fault feature information of rolling bearings, and its analysis results are better than those using the direct fast spectral correlation method and the one combining the wavelet threshold de-noising with the fast spectral correlation, respectively;it provides an effective way for extracting weak fault features of rolling bearings.
作者
唐贵基
田甜
庞彬
TANG Guiji;TIAN Tian;PANG Bin(Department of Mechanical Engineering, North China Electric Power University, Baoding 071003 , China)
出处
《振动与冲击》
EI
CSCD
北大核心
2019年第11期187-193,227,共8页
Journal of Vibration and Shock
基金
国家自然科学基金(51307058)
中央高校基本科研业务专项基金(2017XS134)
关键词
快速谱相关
总变差去噪
故障诊断
特征提取
fast spectral correlation
total variation de-noising
fault diagnosis
feature extraction
