Bearing fault diagnosis using Wavelet analysis

Kang Chen, Xiaobing Li, Feng Wang, Tanglin Wang, Cheng Wu
{"title":"Bearing fault diagnosis using Wavelet analysis","authors":"Kang Chen, Xiaobing Li, Feng Wang, Tanglin Wang, Cheng Wu","doi":"10.1109/ICQR2MSE.2012.6246326","DOIUrl":null,"url":null,"abstract":"One-dimensional discrete wavelet transform is used to process the bearing fault signal in this paper. Firstly, the bearing fault data is decomposed to multi-layer. Then the fault feature signal is reconstructed. In order to detect the bearing failure and determine the area of it, the reconstructed signal is processed by the Hilbert transform demodulation and spectrum refining. The results show that the frequency of failure point matches well with theoretical one using this method. This method is simple and reliable and thus provides a scientific method for early warning and exclusion of failure.","PeriodicalId":401503,"journal":{"name":"2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICQR2MSE.2012.6246326","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27

Abstract

One-dimensional discrete wavelet transform is used to process the bearing fault signal in this paper. Firstly, the bearing fault data is decomposed to multi-layer. Then the fault feature signal is reconstructed. In order to detect the bearing failure and determine the area of it, the reconstructed signal is processed by the Hilbert transform demodulation and spectrum refining. The results show that the frequency of failure point matches well with theoretical one using this method. This method is simple and reliable and thus provides a scientific method for early warning and exclusion of failure.
基于小波分析的轴承故障诊断
本文采用一维离散小波变换对轴承故障信号进行处理。首先,对轴承故障数据进行多层分解;然后重构故障特征信号。为了检测轴承故障并确定故障区域,对重构信号进行希尔伯特变换解调和频谱细化处理。结果表明,该方法得到的故障点频率与理论值吻合较好。该方法简便可靠,为故障的早期预警和排除提供了科学的方法。
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