摘要
针对现场采集的信号因混有噪声而使故障特征提取困难的问题,基于奇异值分解(SVD)、改进的集合经验模态分解(MEEMD)、差分进化算法(DE)、在线贯序极限学习机(OSELM),提出了一种基于改进MEEMD与DE-OSELM的滚动轴承故障诊断方法.首先进行MEEMD分解,对MEEMD算法中经排列熵筛选出的异常IMF分量进行SVD降噪,与剩余信号重构后直接进行EMD分解;其次提取各IMF分量的能量作为特征构造特征集;最后将获得的特征集作为DE-OSELM的输入进行训练并识别故障类型.对实际4种不同健康状态的滚动轴承样本进行分类识别,并与常用分类方法进行比较.结果表明:该方法具有更高的准确率,可以更有效地实现故障诊断.
In order to solve the difficult problem of fault feature extraction due to the mixed noise in the field collected signals,a rolling bearing fault diagnosis method based on improved MEEMD and DE-OSELM was proposed based on SVD,EEMD,DE and OSELM.Firstly,the signal was decomposed by improved MEEMD,the abnormal IMF components selected by permutation entropy were denoised by SVD,and the denoised signal and the remaining signal were reconstructed by EMD.Secondly,the energy of each IMF component was extracted as the feature set.Finally,the obtained feature set was used as the input of DE-OSELM to train and identify the fault type.The actual four types of rolling bearing samples with different health conditions were classified and identified,and compared with common classification methods,the results showed that the proposed method had higher accuracy and could effectively realize fault diagnosis.
作者
蒋永华
黄涛涛
李刚
焦卫东
徐翠
夏海成
王晨
JIANG Yonghua;HUANG Taotao;LI Gang;JIAO Weidong;XU Cui;XIA Haicheng;WANG Chen(Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province,Zhejiang Normal University,Jinhua 321004,China;Xingzhi College,Zhejiang Normal University,Lanxi 321100,China)
出处
《浙江师范大学学报(自然科学版)》
CAS
2021年第4期395-403,共9页
Journal of Zhejiang Normal University:Natural Sciences
基金
国家自然科学基金资助项目(51405449
51575497)
浙江省城市轨道交通智能运维技术与装备重点实验室自主研究课题(ZSDRTZZ2020002)。
关键词
MEEMD
排列熵
差分进化算法
在线贯序极限学习机
故障诊断
MEEMD
permutation entropy
differential evolution algorithm
online sequential extreme learn-ing machine
fault diagnosis