摘要
针对振动传感器在采集故障信号时,在α稳定分布脉冲噪声的干扰下,使得传统机械故障信号时频盲源分离算法性能退化的问题,提出了一种基于分数低阶和S时频变换的盲源分离新方法。该方法先对传感器测试信号进行分数低阶子空间预白化,再计算低阶化信号的S变换时频分布,最后通过联合近似对角化恢复各个部分的故障源信号。通过计算机仿真实例分析表明,该算法能有效抑制脉冲噪声影响,避免了二阶矩或高阶矩无穷大的缺限,盲源分离效果较好,具有良好的鲁棒性。
The impulsive noise of α-stable distribution is characterized by the nonexistence of the finite second order or higher statistics. The blind source separation based on time-frequency distribution（ TFD-BSS） method was poor invalid under α-stable distributed noise conditions. An improved fractional lower order statistics time-frequency distribution blind source separation algorithm was proposed in this paper. First,the signals were pre-whitening based on fractional lower order statistics and subspace technique,and then the fractional lower order time-frequency distribution of generalized s-transform was computed. Finally,the source signals were obtained by joint approximate digitalization of Eigen-matrices. The simulation results analysis shows that the proposed method is more robust in α-stable distribution interference environments than that of the conventional second order statistics based algorithm.Moreover,the decision overcomes the shortcoming of the second and higher order moment infinity for BSS.
出处
《电子测量与仪器学报》
CSCD
北大核心
2016年第3期440-447,共8页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(61261046)
江西省自然基金(20142BAB207006
20151BAB207013)
江西省教育厅科技基金(GJJ14739
GJJ14721)
九江学院校级科研项目(2013KJ02
2013KJ01)资助
关键词
Α稳定分布
时频分析
S变换
盲源分离
预白化
分数低阶统计量
α-stable distribution
S-transform（ST）
time-frequency analysis
blind source separation（BSS）
pre whitening
fractional lower order statistics（FLOS）
