Adaptive ensemble empirical mode decomposition and its application to fault detection of planetary gearboxes

Empirical mode decomposition (EMD) has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decomposition (EEMD) is proposed accordingly. EEMD can reduce the mode mixing to some extent. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithm. In current studies on EEMD, the parameters are generally selected artificially and subjectively. To solve the problem, a new adaptive ensemble empirical mode decomposition method is proposed. In the method, the sifting number is adaptively selected and the amplitude of the added noise changes with the signal frequency during the decomposition process. Both simulations and a case of fault detection of a planetary gear demonstrate that the proposed method obtains the improved results compared with the original EEMD.