摘要
对振动信号具有非平稳性,结合时频分析和盲分离技术的特点,提出基于Gabor变换的盲分离算法.该方法利用Gabor展开的线性时频变换特性,根据Gabor展开系数求出近似混合矩阵,实现盲信号分离.数值仿真和试验结果表明,该方法能实现多分量信号的分离,再根据各分离信号中的频谱,便能准确地得到轴承故障特征频率,有效地提取多种轴承故障信息,为故障诊断提供一种新的研究方向.
Aimed at the non-stationarity of the signals,an algorithm of blind signal separation （BBS） was proposed based on Gabor transform and by combining the characteristics of time-frequency analysis and blind signal separation.In this algorithm,the characteristics of Gabor expansion with a linear time-frequency transform was used to find approximate mixed matrix from Gabor expansion coefficient,so that the separation of blind signals was realized.The results of numeric simulation and experiment showed that with this method,separation of multi-component signals could be implemented and the fault characteristic frequency of the bearing could be identified accurately on the basis of frequency spectrum of the separated signals and various fault information of the bearing were extracted effectively,providing a new research direction for fault diagnosis.
出处
《兰州理工大学学报》
CAS
北大核心
2013年第6期40-44,共5页
Journal of Lanzhou University of Technology
关键词
时频分析
GABOR变换
故障诊断
盲信号分离
time-frequency analysis
Gabor transform
fault diagnosis
blind signal separation
