Fault feature extraction based on optimal energy using lifting wavelet packet

Xiaoli Xu, Tao Chen, Shao-hong Wang
{"title":"Fault feature extraction based on optimal energy using lifting wavelet packet","authors":"Xiaoli Xu, Tao Chen, Shao-hong Wang","doi":"10.1109/ICIST.2011.5765310","DOIUrl":null,"url":null,"abstract":"Fault prediction is the key technology to guarantee the safe operation of large mechanical equipment,and fault feature extraction is a key issue in fault prediction. To extract fault feature from the non-stationary fault signals, this paper proposed a fault feature extraction method using lifting wavelet packet, and constructed the fault feature vector of optimal energy. The fault feature extraction analysis shows that the proposed method can highlight the energy change within the optimal decomposition frequency band, and effectively reflect the fault status.","PeriodicalId":6408,"journal":{"name":"2009 International Conference on Environmental Science and Information Application Technology","volume":"109 1","pages":"548-551"},"PeriodicalIF":0.0000,"publicationDate":"2011-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Environmental Science and Information Application Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIST.2011.5765310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Fault prediction is the key technology to guarantee the safe operation of large mechanical equipment,and fault feature extraction is a key issue in fault prediction. To extract fault feature from the non-stationary fault signals, this paper proposed a fault feature extraction method using lifting wavelet packet, and constructed the fault feature vector of optimal energy. The fault feature extraction analysis shows that the proposed method can highlight the energy change within the optimal decomposition frequency band, and effectively reflect the fault status.
基于最优能量的提升小波包故障特征提取
故障预测是保证大型机械设备安全运行的关键技术,故障特征提取是故障预测中的关键问题。为了从非平稳故障信号中提取故障特征,提出了一种提升小波包的故障特征提取方法,并构造了能量最优的故障特征向量。故障特征提取分析表明,该方法能突出最优分解频带内的能量变化,有效反映故障状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信