Rolling bearing is one of the important parts of traction motor, and the accuracy of its fault diagnosis is of great significance to ensure the normal operation of traction motor.In order to improve the accuracy and effectiveness of bearing fault diagnosis, the method of maximum correlation kurtosis deconvolution (MCKD) combined with Hilbert-Huang transform (HHT) is used for diagnosisIn view of the selection of MCKD algorithm subject to shift number (M), filter order (L) and shock signal period (T), it is particularly dependent on the choice of experience. The dynamic particle swarm algorithm is selected to optimize it to reduce noise signal interference. Pulse signal triggered by fault.Then use the HHT algorithm to get the signal envelope spectrum, which can better identify different types of faults. Combining VS with MATLAB can realize the application of diagnostic algorithms to the high-level development language environment.The algorithm was verified using the CWRU bearing data set. The verification results show that the method can effectively enhance the fault characteristics. The fault frequency of the bearing inner ring is 162Hz and the bearing inner ring fault frequency is 108Hz, which can accurately identify the fault type of the bearing.