A Classification Method For Rotor Imbalance Fault With Isfla-Svm

In this paper, a classification method for rotor imbalance fault (RIF) using support vector machine (SVM) is proposed. It adopts an improved shuffled frog-leaping algorithm (ISFLA) to optimize the parameters of SVM. Given the nonuniformity and the defect of trapping into the local optimum solution of the initial population existed in SFLA, some improvement methods are presented in ISFLA-SVM. ISFLA employs random uniform design (RUD) to generate an initial population. Besides, the global optimum solution of the proposed method could be found by changing the updating strategy of Xw in the subgroup. The performance of these three classification algorithms, i.e., particle swarm optimization (PSO)-SVM, SFLA-SVM, and ISFLA-SVM are compared. Analysis results show that ISFLA-SVM has the highest recognition accuracy.