摘要
提出了一种基于核独立分量分析(KICA)的非线性混合机械故障源盲分离方法,即利用核函数的优点,将信号从低维的非线性原始空间变换到高维线性特征空间,实现以线性ICA方法进行分离。仿真结果表明:与传统的ICA方法相比,本方法在处理非线性混合源盲分离上具有明显的优势,并在轴承故障信号盲分离实验中验证了它的有效性。
A nonlinear mixture blind separation method of the mechanical fault sources based on kernel independent component analy-sis is proposed. In the proposed method, the signal is transformed from the low - dimensional nonlinear original space into a high - dimensional linear feature space by the kernel function, so that nonlinear mixture mechanical fault sources can be separated by the linear ICA method. The simulation result shows that the proposed method is superior to the traditional ICA method in processing nonlinear mixed blind separation problem. Finally the proposed method is applied to the blind separation of the bearing fault. The experiment re- sult further verifies the validity of the proposed method.
出处
《南昌航空大学学报(自然科学版)》
CAS
2012年第4期45-49,共5页
Journal of Nanchang Hangkong University(Natural Sciences)
基金
国家自然科学基金(51075372
50775208)
江西省教育厅科技计划项目(GJJ12405)
关键词
盲源分离
核独立分量分析
故障诊断
blind source separation
kernel independent component analysis
fault diagnosis