A classification method for rotor imbalance fault with ISFLA-SVM

摘要

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 non-uniformity 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 X_w 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.