Fault feature extraction of large wind turbine main bearing using self-adaption EEMD
ZHANG Lei
CHEN Chang-zheng
LIU Jie
Abstract：The EEMD is a suited analysis method for nonlinear and non-stationary signal but the selection of the standard deviation of added white noise σand the number of ensemble number N are mainly based on expe-rience.In this paper, the self-adaption value σand N, are obtained by simulation experiment and the self-ad-aptation EEMD is used for the fault feature extraction of large wind turbine main bearing .It is manifested that self-adaptation EEMD is adept in inhibition of modal aliasing by the instance analysis .The self-adaptation EE-MD can be used in the condition monitoring and fault diagnosis of large wind turbine main bearing as a pre -pro-cessing algorithm.
Keywords：self-adaptationEEMDwind turbinefault feature extraction
Publication Date：2015-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 5-8 )