GEARBOX DIAGNOSIS BASED ON SVM OPTIMIZED BY DOUBLE GROUP COEVOLUTION FRUIT FLY OPTIMIZATION ALGORITHM

In order to improve the optimization effect of the fruit fly optimization algorithm（FOA） on support vector machine（SVM） parameters, the evolution strategy of FOA was improved, and the duoble group coevolution fruit fly optimization algorithm（DGCFOA） was proposed in this paper. The DGCFOA was used to optimize the parameters of SVM and then used to gearbox fault diagnosis. Diagnosis results show that DGCFOA algorithm can obtained better SVM parameters when compared with FOA, it significantly improved fault diagnosis accuracy of gearbox. In addition, the diagnosis results also show that DGCFOA has higher diagnostic accuracy and more obvious advantages when compared with some other methods.