Aiming at the phenomenon that the decomposition of multiple channel signals may not match in either number or frequency content by standard Empirical Mode Decomposition (EMD) and the problem of fault recognition misjudgment caused by using the incomplete information, a method of fault diagnosis for rolling bearing is proposed combining noise assisted multivariate empirical mode decomposition (NAMEMD) with full vector spectrum. At first, a series of Intrinsic Mode Function (IMF) component could be gotten after the NAMEMD adaptively decomposing the multiple sources information compounded of homologous double channel signal and a noise assisted signal. The IMF component containing main fault information was then selected from homologous double channel signal according to correlation coefficients to conduct signal reconstruction. Finally, the full vector information fusion was used to merge the reconstruction signal to extract fault feature. In order to verify the effectiveness of the proposed method, a simulation signal and experimental signal are analyzed.