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he regularization coefficient and the parameters in the wavelet kernel function of the rolling bearing fault diagnosis based on the extreme learning machine will affect the classification effect of WKELM. A fault identification method based on improved FOA algorithm to optimize WKLEM parameters is proposed. The method uses VMD to decompose the fault signal of the rolling bearing to obtain the modal components containing the fault information, and uses SVD to obtain each modal singular value as the feature vector. The LGMS improved FOA algorithm was introduced to optimize the relevant parameters of WKELM, and the optimal rolling bearing fault diagnosis classifier was constructed. The experimental comparison results show that the LGMS-FOA-WKELM method not only has high recognition accuracy, but also has shorter training time and stronger stability.