The low-speed and heavy-load portal crane slewing bearing signal is easily affected by environmental noise and it is difficult to extract fault characteristics.To solve this problem,a fault diagnosis method combining the ensemble empirical mode decomposition (EEMD) and the maximum correlated kurtosis deconvolution (MCKD) optimized by grey wolf optimization (GWO) algorithm was proposed.The slewing bearing signal was decomposed by using EEMD to obtain a series of intrinsic mode functions (IMF),and the IMF with max kurtosis was selected as the optimal component;the correlation kurtosis was taken as the objective function and the GWO was used to find the optimal parameter combination of MCKD on the optimal component;the MCKD of the optimal parameter combination was used to reduce the noise of the optimal component and highlight the fault impact component;the signal after noise reduction was analyzed by using the envelope spectrum,and the fault diagnosis was completed.The results show that by using the proposed method,the fault impact component can be enhanced adaptively,and the fault characteristics can be extracted effectively.