基于小波降噪和希尔伯特黄变换的滚动轴承故障特征提取  

Fault Features Extraction of Rolling Bearing Based on Wavelet De-noising and HHT

作　　者：苏涛[1] 夏均忠 李树珉[3] 白云川 张建生[1] 

机构地区：[1]军事交通学院研究生管理大队,天津300161 [2]军事交通学院军用车辆系,天津300161 [3]军事交通学院外训系,天津300161

出　　处：《军事交通学院学报》2014年第3期52-56,共5页Journal of Military Transportation University

摘　　要：希尔伯特黄变换(HHT)是一种自适应时频处理方法,并运用到滚动轴承故障诊断中,但其对噪声比较敏感。为消除噪声对诊断结果的影响,提出基于小波降噪和希尔伯特黄变换相结合的滚动轴承故障特征提取方法。首先利用小波变换去除振动信号中的随机噪声,然后对降噪后的振动信号进行希尔伯特黄变换,最终得到振动信号的希尔伯特边际谱,提取故障特征。通过仿真和滚动轴承故障实验,验证该方法的有效性。Hilbert- Huang Transform （HHT） is an adaptive time -frequency analysis method; it is successfully applied in rolling beating fault diagnosis. However, HHT method is sensitive to noise. In order to eliminate influence of noise on re- sult of diagnosis, a fault diagnosis approach for rolling bearing based on wavelet de - noising and HHT was proposed. First- ly, wavelet was used to remove noise from vibration signal. Then, the de-noising signal was processed with HHT. Finally, it got Hilbert marginal spectrum of the vibration signal and the fault features were extracted. The result between the simula- tion data and the actual rolling beating fault diagnosis tests data shows that the proposed method is effective.

关 键 词：滚动轴承 小波降噪 希尔伯特黄变换 故障特征提取 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]