Local characteristic-scale decomposition(LCD)was a new non-stationary data analysis method

which was proposed recently and similar to empirical mode decomposition(EMD).In order to solve its mode mixing problem

firstly a noise-assisted data analysis method named ensemble local characteristic-scale decomposition(ELCD)is presented.However

since ELCD inherited the shortcomings of ensemble empirical mode decomposition(EEMD)and complementary ensemble empirical mode decomposition(CEEMD)

in the same time

based on the new randomicity detecting method-permutation entropy(PE)

another method for restraining mode mixing called partly ensemble local characteristic-scale decomposition(PELCD)had been proposed in this paper.Lastly

the novel method was compared with the existing method(CEEMD)by analyzing simulation data and real data and the results indicate that the proposed method can restrain the phenomenon of mode mixing effectively and is superior to ELCD and other traditional noise-assisted method in aspects of inhibiting false components and improving the accuracy of components.