中南大学学报（自然科学版）2017，Vol.48Issue(1)：61-68,8.DOI:10.11817/j.issn.1672-7207.2017.01.009
基于Teager能量算子和深度置信网络的滚动轴承故障诊断
Fault damage degrees diagnosis for rolling bearing based on Teager energy operator and deep belief network
摘要
Abstract
Considering that the traditional classifiers' generalization ability is not strong in the early fault diagnosis of rolling bearings,the fault diagnosis method based on Teager energy operator (TEO) and deep belief network (DBN) were put forward.Firstly,the instantaneous amplitudes of the vibration signal were calculated by TEO,and the instantaneous energies of the signal were extracted.Then the characteristic vectors were constituted with the instantaneous energies.DBN classifiers were used to identify the faults of rolling bearing.For different types of fault diagnosis,DBN structure parameters were adjusted according to the classification error rate of training sets.Using the bearing fault experiments' data of American West Storage University,the classification accuracy of SVM and KNN was compared.The results show that the suggested methods are more effective and stable for the identification of rolling bearing fault diagnosis in various situations.关键词
深度置信网络/Teager能量算子/滚动轴承/故障诊断Key words
deep belief network/Teager energy operator/rolling bearings/fault diagnosis分类
通用工业技术引用本文复制引用
陶洁,刘义伦,付卓,杨大炼,汤芳..基于Teager能量算子和深度置信网络的滚动轴承故障诊断[J].中南大学学报（自然科学版）,2017,48(1):61-68,8.基金项目
国家自然科学基金资助项目(51375500) （51375500）
国家重点基础研究发展计划(973计划)项目(2014CB046300) （973计划）
湖南省科技计划项目(2016GK2005) (Project(51375500) supported by the National Natural Science Foundation of China （2016GK2005）
Project(2014CB046300) supported by the National Basic Research Development Program (973 Program) of China （2014CB046300）
Project(2016GK2005) supported by Key Research and Development Projects of Hunan Province) （2016GK2005）
