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Wind turbine bearings usually work with strong background noise, making the faulty properties difficult to extract and detect. To accurately diagnose the faults of rolling bearings in wind turbines, an iterative modified adaptive chirp mode decomposition (IMACMD) method is proposed in this paper. Firstly, an envelope interpolation method is employed to preliminarily determine the iterative mode number and guide the potentially initial frequency selection. Secondly, the upper limits of the iterative mode number and the initial frequency are further determined through correlation analysis. During the iteration process, the optimal weight factor of the reconstructive input signal, which is the residual signal of the previous iterative decomposition, is determined according to the new designed ensemble <i>L</i>-Kurtosis index. Experimental and engineering signals are used to validate the proposed IMACMD method. Comparisons with the conventional methods demonstrate the superiority of this proposed method. It is shown that this method can not only identify the weak features for single faults but also separate the multiple features for compound faults.