Due to the importance of rolling bearing as one of the most widely used in rotating machines

bearing failures have adverse effects on the safe operation of the equipment

in order to diagnosing the fault of rolling bearing effectively

a fault diagnosis model of support vector machine（SVM）optimized by quantum particle swarm optimization（QPSO）algorithm is proposed.First

fault vibration signals are decomposed into several intrinsic mode functions（IMFs）using empirical mode decomposition（EMD）method

then the instantaneous amplitudes of the IMFs that have the fault characteristics are extracted and regarded as the features vector

finally the SVM model optimized by QPSO is used for the failure mode identification.The experimental results indicate that the proposed bearing fault diagnosis method has good capability for adaptive features extraction as well as high fault diagnostic accuracy.