机械与电子2018，Vol.36Issue(5)：33-36,41,5.
QPSO-WT和QPSO-SVM在滚动轴承故障诊断中的应用
The Applications of QPSO-WT and QPSO-SVM in Fault Diagnosis of Rolling Bearing
摘要
Abstract
For the problems of the wavelet threshold is not global optimal solution and punishment pa-rameter and kernel function parameter setting problem in SVM algorithm,improved filtering algorithm and recognition algorithm based on wavelet threshold and SVM and quantum-behaved particle swarm op-timization (QPSO)are proposed to improve above questions,and then applying this method to extract fea-tures in rolling bearing fault diagnosis.In experiments,QPSO-WT is better than traditional wavelet threshold in filtering,ten bearings with different conditions were diagnosed by QPSO-SVM,getting the result that Accuracy is as high as 87.67%,and Comparing with SVM and RBF neural network further confirmed the effectivity of this method.关键词
量子行为粒子群/小波变换/支持向量机/参数寻优/故障诊断Key words
quantum-behaved particle swarm optimization (QPSO)/wavelet transform/SVM/pa-rameter optimization/fault diagnosis分类
机械制造引用本文复制引用
张思聪,傅攀,蒋恩超,朱奥辉..QPSO-WT和QPSO-SVM在滚动轴承故障诊断中的应用[J].机械与电子,2018,36(5):33-36,41,5.基金项目
中央高校基本科研业务费专项资金资助(2682016CX033) （2682016CX033）
