A Fast and Adaptive Empirical Mode Decomposition Method and Its Application in Rolling Bearing Fault Diagnosis

Although the ensemble empirical mode decomposition ( EEMD ) method and the complementary ensemble empirical mode decomposition (CEEMD) method can greatly improve the mode mixing of the original empirical mode decomposition (EMD) method, the difference in white noise amplitude and the number of ensemble trials have a great influence on the decomposition results. And the low computational efficiency can not meet the requirements of the online monitoring system. To solve these problems, a fast and adaptive empirical mode decomposition (FAEMD) method was proposed in this paper, which combines the advantages of the order statistics filter (OSF) with the original EMD. First, the upper envelope and lower envelope of the original signal were drawn by using the order statistical filter. Then the original signal was decomposed into a series of intrinsic mode functions (IMFs) by the mean envelopes. Finally, the fault feature information was obtained by using the envelope spectrum analysis. The simulation signal and two groups of test fault signals were taken as examples to verify the effectiveness of the proposed method. Compared with EMD, CEEMD, and CEEMDAN methods, FAEMD can effectively extract the key feature information of fault signals and has strong practicability because of the low calculation cost.