The invention relates to a method for optimizing a multi-kernel multi-
feature fusion support vector machine and identifying a bearing fault. The method comprises a step of selecting bearing vibrationsignals collected under a single sensor, a step of decomposing
bearing vibration signals at different rotational speeds by EMD to obtain IMF energy entropy and IMF
permutation entropy, a step of extracting IMF energy entropy and IMF
permutation entropy at different rotation speeds and fusing the IMF energy entropy and IMF
permutation entropy to obtain fusion features including different rotationalspeed information for
support vector machine training samples so as to obtain the multi-kernel multi-
feature fusion support vector machine which is adapted to fault identification at different rotation speeds, a step of integrating
Gaussian radial basis function kernel and polynomial function kernel performance, allowing the training samples to be in
linear regression from a nonlinear function space to high-dimensional
space mapping such that the training samples are classified according to different characteristics, forming a multi-kernel least square support vector
machine, and enabling thesupport vector
machine to identify a fault feature under a
variable load, and a step of carrying out parameter optimization on the training samples with a self-adjusting
particle swarm algorithm withstrong convergence, comparing the training samples and a
test sample, and identifying the bearing fault.