Jixie chuandong (Jan 2018)
Application of ELMD-MCKD in Rolling Bearing Fault Diagnosis
Abstract
Aiming at the problem that the fault information of rolling bearing is weak and the characteristic frequency is difficult to be identified under strong noise environment,the method of fault diagnosis based on the ensemble local mean decomposition（ ELMD） and the maximum correlated kurtosis deconvolution（ MCKD） is proposed,and it is used to handle the bearing fault vibration signal. Firstly,the original data is decomposed into a set of product functions（ PF） by ELMD. Then,each PF component is subjected to noise reduction processing by MCKD. Finally,the PF component of each noise reduction is obtained by finding the envelope spectrum,so as to find the fault characteristic frequency of the bearing in the envelope. In order to verify the effectiveness of ELMD-MCKD in detecting faults,a series of bearing failure simulation experiments are carried out.The results show that the proposed method of ELMD-MCKD can improve the accuracy of bearing fault identification and can be used in fault diagnosis in practical application.
