Fault classification method of rolling bearing based on FOA-MKSVM

Because of the uneven characteristics of rolling bearing actual state data, the single-kernel function used in classification stage has certain limitation. Aiming at this problem and the blindness of multi-parameter selection of support vector machine, a multi-kernel support vector machine model is proposed based on fruit fly optimization algorithm (FOA). The actions of global kernel function and local kernel function in the model can be adjusted with kernel function weight, and the model has both good learning ability and generalization ability. Meanwhile, a certain relation between the parameters of the multi-kernel support vector machine and the smell concentration of the food in FOA is built; by imitating fruit fly foraging behavior, the parameters can be optimized and selected. In order to verify the effectiveness of the proposed method, UCI standard data set was first used in the experiment, then the method was applied in rolling bearing fault classification. The single-kernel function, multi-kernel function and parameter optimization algorithms were compared, the results show that the proposed method has some advantages, such as less initialization parameters, simple parameter setting, strong global search ability and high classification accuracy rate, and can be effectively applied in rolling bearing fault classification.