Core consistency blind source separation based on compressive sensing trilinear decomposition

In the traditional fault diagnosis method based on blind source separation, the mechanical signals are required to meet some additional conditions in the process of estimation of mixed matrix and separation of source signals, which may cause many problems in practice application because the mechanical signals do not suffer to these additional conditions. Additionally, the trilinear model has complex construction, high computational complexity, and large storage capacity in the fault source blind separation model with trilinear parallel factors. Based on the above deficiencies, a core consistency blind source separation method is proposed based on compressive sensing trilinear decomposition. In the proposed method, the core consistency diagnostic (CORCONDIA) is used to fit the tensor model, and determine the number of fault sources. The mixed matrix of the observed signal is estimated by the load matrices. Then, the minimum norm method is used to solve the underdetermined blind separation of fault sources. The simulation and experiment results show that the proposed method is superior the traditional core consistency blind separation method based on trilinear parallel factor analysis in the underdetermined blind separation. The proposed method can effectively solve the problem that the number of sources is unknown and the number of observed signals is less than the number of source signals.