A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE - WU YinHua; XU QiongYan (Jixie qiangdu)
Aiming at the fact that the fault diagnosis performance of support vector machine（ SVM） highly depends on the parameters selection,a fault diagnosis method based on improved artificial bee colony（ IABC） optimize SVM was proposed. In order to improve search ability of ABC,Levy flight strategy was introduced and improved the original ABC algorithm. Use the IABC to optimize SVM parameters can effectively improve the classification performance of SVM. Different fault type and different fault degree of rolling bearing fault diagnosis experiment results show that the IABC can obtain better parameters when compared with ABC,GA and PSO,improved the fault diagnosis accuracy of SVM and can applied to fault diagnosis efficiently