Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals;a single channel blind source separation method combining EEMD, PCA ...Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals;a single channel blind source separation method combining EEMD, PCA and RobustICA is proposed. Through the eemd decomposition of the single-channel mechanical vibration observation signal the multidimensional IMF components are obtained, and the principal component analysis (PCA) is performed on the matrix of these IMF components. The number of principal components is determined and a new matrix is generated to satisfy the overdetermined blind source separation conditions, the new matrix input RobustICA, to achieve the separation of the source signal. Finally, the isolated signals are respectively analyzed by the envelope spectrum, the fault frequency is extracted, and the fault type is judged according to the prior knowledge. The experiment was carried out by using the simulation signal and the mechanical signal. The results show that the algorithm is effective and can accurately diagnose the location of mechanical fault.展开更多
针对含噪环境下数字调制混合信号盲源分离（BSS）误码率（BER）过高的问题,提出了一种基于Robust ICA的二阶段盲源分离算法R-TSBS。该算法采用Robust ICA算法对阵列响应向量构成的混合矩阵进行估计,然后利用数字调制信号的有限符号集特...针对含噪环境下数字调制混合信号盲源分离（BSS）误码率（BER）过高的问题,提出了一种基于Robust ICA的二阶段盲源分离算法R-TSBS。该算法采用Robust ICA算法对阵列响应向量构成的混合矩阵进行估计,然后利用数字调制信号的有限符号集特征,在第二阶段用最大似然估计（MLE）方法估计各个数字调制源信号发送的符号序列,达到盲源分离的目的。实验仿真表明,传统的独立成分分析（ICA）算法如Robust ICA算法和Fast ICA算法误码率很高,在信噪比（SNR）为10 d B时,其误码率达到了3.5×10-2左右,而基于Fast ICA的二阶段盲源分离算法F-TSBS和基于Robust ICA的二阶段盲源分离算法R-TSBS的误码率则下降到了10-3,分离性能得到了明显改善;在较低的信噪比（0~4 d B）下,R-TSBS算法较F-TSBS算法约有2 d B性能提升。展开更多
文摘Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals;a single channel blind source separation method combining EEMD, PCA and RobustICA is proposed. Through the eemd decomposition of the single-channel mechanical vibration observation signal the multidimensional IMF components are obtained, and the principal component analysis (PCA) is performed on the matrix of these IMF components. The number of principal components is determined and a new matrix is generated to satisfy the overdetermined blind source separation conditions, the new matrix input RobustICA, to achieve the separation of the source signal. Finally, the isolated signals are respectively analyzed by the envelope spectrum, the fault frequency is extracted, and the fault type is judged according to the prior knowledge. The experiment was carried out by using the simulation signal and the mechanical signal. The results show that the algorithm is effective and can accurately diagnose the location of mechanical fault.
文摘针对含噪环境下数字调制混合信号盲源分离（BSS）误码率（BER）过高的问题,提出了一种基于Robust ICA的二阶段盲源分离算法R-TSBS。该算法采用Robust ICA算法对阵列响应向量构成的混合矩阵进行估计,然后利用数字调制信号的有限符号集特征,在第二阶段用最大似然估计（MLE）方法估计各个数字调制源信号发送的符号序列,达到盲源分离的目的。实验仿真表明,传统的独立成分分析（ICA）算法如Robust ICA算法和Fast ICA算法误码率很高,在信噪比（SNR）为10 d B时,其误码率达到了3.5×10-2左右,而基于Fast ICA的二阶段盲源分离算法F-TSBS和基于Robust ICA的二阶段盲源分离算法R-TSBS的误码率则下降到了10-3,分离性能得到了明显改善;在较低的信噪比（0~4 d B）下,R-TSBS算法较F-TSBS算法约有2 d B性能提升。
