Abstract:A combined method was proposed which employs ensemble empirical mode decomposition (EEMD), singular value decomposition (SVD) and locality preserving projection (LPP) to extract useful fault feature from the complex vibration signals of rolling element bearings with problems of extraction in difficulty. Firstly, the vibration signals were decomposed with EEMD into a set of intrinsic mode functions(IMFs), which then were utilized to construct the timedomain and frequencydomain spatial condition matrix, as well as the timefrequency domain spatial condition matrix. Secondly, SVD was used to extract the fault information of multipledomain spatial condition matrix and among which selected the effective SVs which cumulative percentage were greater than 90% constituted the multipledomain SV feature matrix. Thirdly, LPP was used to extract the lowdimension and highseparability fault features from multipledomain SV feature matrix. Finally, support vector machine (SVM) was used to evaluate the fault feature extraction method proposed. The experimental result illustrate that the fault feature extracted according to this method can effectively reflect the fault patterns of rolling element bearings.