Empirical mode decomposition (EMD), as an adaptive signal decomposition method, has been widely applied to many engineering fields. Two kinds of mean optimized empirical mode decomposition (MOEMD) methods are developed to improve the decomposition performance of EMD. The first method is based on selecting decomposition results with the smallest orthogonality that obtained by different weighted mean curve iteration sifting as the optimal one and the second is selecting the optimal intrinsic mode function in each layer from the results obtained by different weighted mean curve iteration sifting to ensure an overall optimal decomposition result. The two MOEMD algorithms are compared with EMD and other existing signal decomposing methods by analyzing simulation signals and the comparison results indicate that the proposed methods have much more significantly improvement in decomposition performance and precision than EMD and other methods. Finally, the proposed methods are applied to the rotor rubbing fault signal analysis by comparing with EMD and the results show that MOEMD method could effectively identify the rotor rubbing fault and get much better recognition effect than EMD.
ZHENG Jinde
,
PAN Haiyang
,
CHENG Junsheng
. Mean-optimized Empirical Mode Decomposition and Its Application in Rotor Fault Diagnosis[J]. Journal of Mechanical Engineering, 2018
, 54(23)
: 93
-101
.
DOI: 10.3901/JME.2018.23.093
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