In allusion to the determination of the kernel parameters and the effective evaluation of the clustering results of Fuzzy Kernel-clustering Algorithm (FKCA), differential evolution algorithm (EA) is used to search the optimal kernel parameter and the clustering centers. Furthermore, the Xie-Beni index is promoted to the kernel space, and a new fitness function is designed to improve the clustering performance. The proposed method is applied in the standard benchmark as well as the motor bearing fault dataset. The results shows that the proposed method is a promising clustering method for fault diagnosis.