基于改进SSD降噪的滚动轴承故障特征提取  

Feature Extraction of Weak Fault for Rolling Bearing based on Improved SSD Denoising

作　　者：王续鹏 孙虎儿[1] Wang Xupeng;Sun Huer(School of Mechanical Engineering,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学机械工程学院,山西太原030051

出　　处：《机械传动》2022年第3期163-169,共7页Journal of Mechanical Transmission

摘　　要：针对强背景噪声下滚动轴承早期微弱故障特征难以提取以及奇异谱分解方法分解的分量仍然包含噪声的问题,提出了一种奇异谱分解(Singular spectrum decomposition,SSD)和最大循环平稳盲解卷积(Maximum cyclostationarity blind deconvolution,CYCBD)相结合的滚动轴承微弱故障特征提取方法。由SSD方法将轴承振动信号自适应地分解为从高频到低频的奇异谱分量;根据分量峭度最大原则,筛选出最佳分量;再利用CYCBD对最佳分量后处理进一步降噪;进而对降噪后的信号进行Hilbert包络解调分析,得到故障特征频率。仿真和实验分析表明,该方法能有效提取滚动轴承早期微弱故障特征。Aiming at the problem of early weak fault features of rolling bearings are difficult to be extracted under strong background noise and the components decomposed by the singular spectral decomposition method still contain noise,a method of extracting the weak fault features of rolling bearing based on the combination of singular spectrum decomposition(SSD)and maximum cyclostationarity blind deconvolution(CYCBD)is proposed. The SSD method is used to adaptively decompose the bearing vibration signal into high-frequency to low-frequency singular spectral components. The best component is selected according to the principle of maximum component kurtosis. The best component is used in CYCBD post-processing for further noise reduction.Furthermore,the noise reduced signal is analyzed by Hilbert envelope demodulation to obtain the fault characteristic frequency. Simulation and experimental analysis show that this method can extract early weak fault features of rolling bearings effectively.

关 键 词：滚动轴承 奇异谱分解 最大2阶循环平稳盲解卷积 微弱故障 特征提取 

分 类 号：TH133.33[机械工程—机械制造及自动化]