利用快速傅立叶变换和小波工具验证感应电机模型

F. Villalobos-Pina, R. Álvarez-Salas, E. Cabal-Yépez, A. García-Perez
{"title":"利用快速傅立叶变换和小波工具验证感应电机模型","authors":"F. Villalobos-Pina, R. Álvarez-Salas, E. Cabal-Yépez, A. García-Perez","doi":"10.1109/DEMPED.2013.6645716","DOIUrl":null,"url":null,"abstract":"This paper presents a comparative validation for electric stator and rotor faults through an induction-motor modeling utilizing Park Instantaneous Space Phasor (ISP) during stator current analysis and fast Fourier Transform (FFT2) to identify the fault spectrum and the band spectral density of wavelet coefficients using multi-resolution analysis (MRA). The spectral analysis identifies the fault signature modifying the sample frequency in the data acquisition system. The wavelet analysis maintains a constant sample frequency using MRA, which provides redundant information to identify the faults. Furthermore, the MRA analysis of ISP stator currents helps to identify small incipient faults choosing a threshold between the healthy and faulty machine. The cases considered are stator and rotor electric faults, which are modeled by means of parametric variations. In Spite of its simplicity, the model provides useful information for fault identification.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Induction motor model validation using fast fourier transform and wavelet tools\",\"authors\":\"F. Villalobos-Pina, R. Álvarez-Salas, E. Cabal-Yépez, A. García-Perez\",\"doi\":\"10.1109/DEMPED.2013.6645716\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a comparative validation for electric stator and rotor faults through an induction-motor modeling utilizing Park Instantaneous Space Phasor (ISP) during stator current analysis and fast Fourier Transform (FFT2) to identify the fault spectrum and the band spectral density of wavelet coefficients using multi-resolution analysis (MRA). The spectral analysis identifies the fault signature modifying the sample frequency in the data acquisition system. The wavelet analysis maintains a constant sample frequency using MRA, which provides redundant information to identify the faults. Furthermore, the MRA analysis of ISP stator currents helps to identify small incipient faults choosing a threshold between the healthy and faulty machine. The cases considered are stator and rotor electric faults, which are modeled by means of parametric variations. In Spite of its simplicity, the model provides useful information for fault identification.\",\"PeriodicalId\":425644,\"journal\":{\"name\":\"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DEMPED.2013.6645716\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DEMPED.2013.6645716","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文利用定子电流分析中的Park瞬时空间相量(ISP)和快速傅立叶变换(FFT2)识别故障谱和多分辨率分析(MRA)小波系数的频带谱密度,通过感应电机建模对定子和转子电气故障进行了对比验证。在数据采集系统中,频谱分析通过修改采样频率来识别故障特征。小波分析利用MRA保持恒定的采样频率,为故障识别提供冗余信息。此外,ISP定子电流的MRA分析有助于识别小的早期故障,在健康和故障机器之间选择阈值。考虑的情况是定子和转子电气故障,并采用参数变分的方法进行建模。该模型虽然简单,但为故障识别提供了有用的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Induction motor model validation using fast fourier transform and wavelet tools
This paper presents a comparative validation for electric stator and rotor faults through an induction-motor modeling utilizing Park Instantaneous Space Phasor (ISP) during stator current analysis and fast Fourier Transform (FFT2) to identify the fault spectrum and the band spectral density of wavelet coefficients using multi-resolution analysis (MRA). The spectral analysis identifies the fault signature modifying the sample frequency in the data acquisition system. The wavelet analysis maintains a constant sample frequency using MRA, which provides redundant information to identify the faults. Furthermore, the MRA analysis of ISP stator currents helps to identify small incipient faults choosing a threshold between the healthy and faulty machine. The cases considered are stator and rotor electric faults, which are modeled by means of parametric variations. In Spite of its simplicity, the model provides useful information for fault identification.
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:481959085
Book学术官方微信