Motor Torque Analysis for diagnosis in PMSMs under non-stationary conditions

A. Bonci, Renat Kermenov, S. Longhi, Giacomo Nabissi
{"title":"Motor Torque Analysis for diagnosis in PMSMs under non-stationary conditions","authors":"A. Bonci, Renat Kermenov, S. Longhi, Giacomo Nabissi","doi":"10.1109/ETFA45728.2021.9613449","DOIUrl":null,"url":null,"abstract":"The field of Permanent Magnet Synchronous Motors (PMSMs) diagnosis is of research interest because widely used both in the Industrial environment and in electric vehicles. Amongst various Fault Detection (FD) techniques, the Motor Current Signature Analysis (MCSA) received lots of attention because some defecting frequencies may be monitored through the motor currents in case of steady-state functioning. This latter assumption is not always fulfilled, such e.g. in robotic systems driven by PMSMs, where constant speed assumption is unrealistic in most of the cases. Furthermore, MCSA in not suitable for systems working under non-stationary conditions without using advanced processing techniques. This work investigates the use of load torque information for motor diagnostic purposes under not constant speed assumption. Simulations and experimental results are presented regarding the use of the proposed Motor Torque Analysis (MTA) to overcome these limits.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETFA45728.2021.9613449","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

The field of Permanent Magnet Synchronous Motors (PMSMs) diagnosis is of research interest because widely used both in the Industrial environment and in electric vehicles. Amongst various Fault Detection (FD) techniques, the Motor Current Signature Analysis (MCSA) received lots of attention because some defecting frequencies may be monitored through the motor currents in case of steady-state functioning. This latter assumption is not always fulfilled, such e.g. in robotic systems driven by PMSMs, where constant speed assumption is unrealistic in most of the cases. Furthermore, MCSA in not suitable for systems working under non-stationary conditions without using advanced processing techniques. This work investigates the use of load torque information for motor diagnostic purposes under not constant speed assumption. Simulations and experimental results are presented regarding the use of the proposed Motor Torque Analysis (MTA) to overcome these limits.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
非平稳条件下永磁同步电机转矩分析诊断
永磁同步电机(PMSMs)在工业环境和电动汽车中都有广泛的应用,因此永磁同步电机的诊断是一个备受关注的研究领域。在各种故障检测(FD)技术中,电机电流特征分析(MCSA)受到了广泛的关注,因为在电机稳态工作的情况下,电机电流可以监测到一些缺陷频率。后一种假设并不总是满足,例如在由pmsm驱动的机器人系统中,恒速假设在大多数情况下是不现实的。此外,如果不使用先进的处理技术,MCSA不适合在非平稳条件下工作的系统。这项工作研究了在非恒定转速假设下,将负载转矩信息用于电机诊断目的。模拟和实验结果提出了关于使用所提出的电机扭矩分析(MTA)来克服这些限制。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
An Optimal Order Assignment Algorithm for Single-Rate Time-Driven AFAP Cyclic Executives Demonstrating Reinforcement Learning for Maintenance Scheduling in a Production Environment Investigation in IoT and 5G architectures for deployment of Artificial Intelligence into urban mobility and production Towards a Robust MMIO-based Synchronized Clock for Virtualized Edge Computing Devices LETRA: Mapping Legacy Ethernet-Based Traffic into TSN Traffic Classes
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1