电机与控制应用2017，Vol.44Issue(1)：6-11,6.
基于蜂群算法的选择性神经网络集成的风机齿轮箱轴承故障诊断
Artificial Bee Colony Algorithm Based on Selective Ensemble for Wind Turbine Gearbox Bearing Fault Diagnosis
摘要
Abstract
Gearbox fault data modeling was very complex and its calculation was not feasible.Artificial bee colony algorithm based selective ensemble was proposed to solve the problem.First of all,gearbox bearing fault data was selected for training every learner.Secondly,all learners were given weight coefficients which compose the weight vector as nectar source individual for artificial bee colony algorithm optimization.Finally,comparison of the optimal weight vector and threshold was used to determine which learner should be eliminated.Through a variety of UCI data set analysis and actual bearing fault data set experiment,the results showed that the diagnosis efficiency of the new algorithm was obviously higher than that of genetic algorithm based selective ensemble and the diagnosis accuracy of both methods were fairly even new algorithm was dominant.关键词
集成学习/蜂群算法/神经网络/齿轮箱/故障诊断Key words
ensemble learning/artificial bee colony algorithm/artificial neural network/gearbox/fault diagnosis分类
信息技术与安全科学引用本文复制引用
朱俊,刘天羽,王致杰,黄麒元,孟畅,江秀臣,盛戈皞..基于蜂群算法的选择性神经网络集成的风机齿轮箱轴承故障诊断[J].电机与控制应用,2017,44(1):6-11,6.基金项目
国家自然科学基金资助项目(51477099,11304200) （51477099,11304200）
上海市自然科学基金资助项目(15ZR1417300,14ZR1417200) （15ZR1417300,14ZR1417200）
上海市教委创新基金项目(14YZ157,15ZZ106) （14YZ157,15ZZ106）
