基于维纳滤波的定子电流降噪轴承早期故障检测

Wei Zhou, T. Habetler, R. Harley, B. Lu
{"title":"基于维纳滤波的定子电流降噪轴承早期故障检测","authors":"Wei Zhou, T. Habetler, R. Harley, B. Lu","doi":"10.1109/DEMPED.2007.4393064","DOIUrl":null,"url":null,"abstract":"Current-based monitoring can offer significant economic savings and implementation advantages over traditional vibration monitoring for bearing fault detection. A key issue in current-based bearing fault detection is to extract bearing fault signature from motor stator current. In this paper bearing fault signature in motor stator current is detected by estimating and removing non-bearing fault components via a noise cancellation method. In this method, the components of the stator current that are not related to bearing faults are regarded as noise and are estimated by a Wiener filter. These noise components are then cancelled by their estimates in a real time fashion and a fault indicator is established based on the remaining components that are related to bearing faults. Machine parameters, bearing dimensions, nameplate values, or stator current spectrum distributions are not required in the method. The results of on-line experiments with a 20-horsepower induction motor have verified the effectiveness of this method.","PeriodicalId":185737,"journal":{"name":"2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Incipient Bearing Fault Detection via Stator Current Noise Cancellation using Wiener Filter\",\"authors\":\"Wei Zhou, T. Habetler, R. Harley, B. Lu\",\"doi\":\"10.1109/DEMPED.2007.4393064\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Current-based monitoring can offer significant economic savings and implementation advantages over traditional vibration monitoring for bearing fault detection. A key issue in current-based bearing fault detection is to extract bearing fault signature from motor stator current. In this paper bearing fault signature in motor stator current is detected by estimating and removing non-bearing fault components via a noise cancellation method. In this method, the components of the stator current that are not related to bearing faults are regarded as noise and are estimated by a Wiener filter. These noise components are then cancelled by their estimates in a real time fashion and a fault indicator is established based on the remaining components that are related to bearing faults. Machine parameters, bearing dimensions, nameplate values, or stator current spectrum distributions are not required in the method. The results of on-line experiments with a 20-horsepower induction motor have verified the effectiveness of this method.\",\"PeriodicalId\":185737,\"journal\":{\"name\":\"2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DEMPED.2007.4393064\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DEMPED.2007.4393064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24

摘要

与传统的轴承故障检测振动监测相比,基于电流的监测可以显著节省经济成本并具有实施优势。基于电流的轴承故障检测的一个关键问题是从电机定子电流中提取轴承故障特征。本文采用噪声消除方法对电机定子电流中的非轴承故障分量进行估计和去除,从而检测出电机定子电流中的轴承故障特征。在该方法中,定子电流中与轴承故障无关的分量被视为噪声,并通过维纳滤波器进行估计。然后,这些噪声成分被它们的估计实时取消,并基于与轴承故障相关的剩余成分建立故障指示器。该方法不需要机器参数、轴承尺寸、铭牌值或定子电流谱分布。在一台20马力异步电动机上的在线实验结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incipient Bearing Fault Detection via Stator Current Noise Cancellation using Wiener Filter
Current-based monitoring can offer significant economic savings and implementation advantages over traditional vibration monitoring for bearing fault detection. A key issue in current-based bearing fault detection is to extract bearing fault signature from motor stator current. In this paper bearing fault signature in motor stator current is detected by estimating and removing non-bearing fault components via a noise cancellation method. In this method, the components of the stator current that are not related to bearing faults are regarded as noise and are estimated by a Wiener filter. These noise components are then cancelled by their estimates in a real time fashion and a fault indicator is established based on the remaining components that are related to bearing faults. Machine parameters, bearing dimensions, nameplate values, or stator current spectrum distributions are not required in the method. The results of on-line experiments with a 20-horsepower induction motor have verified the effectiveness of this method.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:604180095
Book学术官方微信