中文摘要：

平行因子（Parallel Factor,PARAFAC）作为一种张量数据处理算法,在宽松约束条件下其模型分解具有唯一性。本文将局域均值分解（Local mean decomposition,LMD）和PARAFAC相结合,提出一种新的欠定盲源分离算法。利用局域均值分解得到观测信号的生产函数（Production functions,PF）分量,再与原观测信号组合得到新的观测信号,从而将欠定混合转换为额定或超定混合源分离问题。对新观测信号进行白化预处理并构造为PARAFAC模型,并利用三线性交替最小二乘（Trilinear Alternating Least Square,TALS）算法实现PARAFAC模型分解,从而得到源信号的估计。通过仿真结果表明LMDPARAFAC算法能够从非平稳欠定混合信号中准确估计源信号。将所提算法应用到多机振动源实验中,实验结果进一步验证了该算法的有效性。

英文摘要：

Parallel Factor is the multidimensional data processing algorithm,which has the uniqueness of the model decomposition under loose constraints. Combining Local mean decomposition with Parallel Factor,a novel underdetermined blind source separation method is proposed. The production functions（ PF） of the original observed signals is obtained by LMD. These PF and original observed signals are combined into new observed signals. Then new signals undergo whitening process and are constructed as the PARAFAC model. Finally the above PARAFAC model is decomposed by trilinear alternating least square（ TALS） and the source signals is precisely estimated. The simulation results show that the proposed algorithm can accurately estimate the vibration source signals from the underdetermined mixtures of non-stationary signal. Finally,the proposed method is applied to the multi-source mechanical vibration test,and the experiment results further verify the effectiveness of the approach.