摘要
针对强背景噪声下滚动轴承微弱故障特征难以提取的特点,提出了基于傅里叶分解(FDM)与1.5维Teager能量谱的滚动轴承故障诊断方法。首先利用傅里叶分解的自适应性特点,将故障信号分解为若干个瞬时频率具有物理意义的固有频带函数,然后利用自相关系数法筛选固有频带函数进行信号重构,对重构后的信号求解1.5维Teager能量谱,从而得到故障特征频率,进行故障诊断。仿真结果表明,与传统的包络谱分析相比,该方法的故障特征更加明显,效果更好。最后将该方法成功地应用到实际的滚动轴承故障诊断中,进一步验证了该方法的有效性。
Based on the characteristic that is the weak fault feature extraction of rolling bearing is very hard under strong background noise,a new method of rolling bearing fault diagnosis based on Fourier decomposition method（ FDM） and 1. 5- dimensional Teager energy spectrum is proposed. The first,the fault signal is decomposed into a number of intrinsic band functions（ FIBFs） which instantaneous frequency has physical significance. Then,the fault signal is reconstituted by using the method of correlation coefficient to screening intrinsic band functions. The last,the fault feature frequency is obtained by using 1. 5- dimensional Teager energy spectrum to the reconstituted signal and the result can be seen. Simulation results show that compared with the traditional envelope spectrum,this method has more obvious fault features and the effect is better. Finally this method is successfully applied to the actual rolling bearing fault diagnosis,further verify the effectiveness of the proposed method.
出处
《机械传动》
CSCD
北大核心
2017年第3期191-196,共6页
Journal of Mechanical Transmission
