• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

一种采用间接测量方法监测三相感应电动机机械功率的低成本物联网传感器网络。

A Low-Cost IoT Sensors Network for Monitoring Three-Phase Induction Motor Mechanical Power Adopting an Indirect Measuring Method.

作者信息

Ciancetta Fabrizio, Fiorucci Edoardo, Ometto Antonio, Fioravanti Andrea, Mari Simone, Segreto Maria-Anna

机构信息

Department of Industrial and Information Engineering and Economics (DIIIE), University of L'Aquila, Piazzale Ernesto Pontieri 1, Monteluco di Roio, 67100 L'Aquila, Italy.

LAERTE Laboratory (Italy), ENEA (Italian National Agency for New Technologies Energy and Sustainable Economic Development), Via Martiri di Monte Sole 4, 40129 Bologna, Italy.

出版信息

Sensors (Basel). 2021 Jan 23;21(3):754. doi: 10.3390/s21030754.

DOI:10.3390/s21030754
PMID:33498639
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7865562/
Abstract

Three-phase induction motors are widely diffused in the industrial environment. Many times, the rated power of three-phase induction motors is not properly chosen causing incorrect operating conditions from an energetic point of view. Monitoring the mechanical dimension of a new motor is helpful, should an existing motor need to be replaced. This paper presents an IoT sensors network for monitoring the mechanical power produced by three-phase induction motors, adopting an indirect measuring method. The proposed technique can be easily adopted to monitor the mechanical power using only one line of current transducer, reducing the cost of the monitoring system. The proposed indirect measurement technique has been implemented on a low-cost IoT system, based on a Photon Particle SoC. The results show that the proposed IoT system can estimate the mechanical power with a relative error of within 8%.

摘要

三相感应电动机在工业环境中广泛应用。很多时候,三相感应电动机的额定功率选择不当,从能量角度来看会导致运行条件不正确。如果需要更换现有电动机,监测新电动机的机械尺寸会有所帮助。本文提出了一种采用间接测量方法的物联网传感器网络,用于监测三相感应电动机产生的机械功率。所提出的技术仅使用一路电流传感器即可轻松用于监测机械功率,降低了监测系统的成本。所提出的间接测量技术已在基于光子粒子系统级芯片的低成本物联网系统上实现。结果表明,所提出的物联网系统能够以8%以内的相对误差估算机械功率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/b959db9a7919/sensors-21-00754-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/b034fea03708/sensors-21-00754-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/091dceb95663/sensors-21-00754-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/6960f055331d/sensors-21-00754-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/f62cdf4c7f2b/sensors-21-00754-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/7fb90d33aaf8/sensors-21-00754-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/807be3c61d12/sensors-21-00754-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/453d7abdfd13/sensors-21-00754-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/e70e69c0d6d9/sensors-21-00754-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/80258495ac2a/sensors-21-00754-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/6afe995eade6/sensors-21-00754-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/ab0d1e6eccd4/sensors-21-00754-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/c0059ff99911/sensors-21-00754-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/b959db9a7919/sensors-21-00754-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/b034fea03708/sensors-21-00754-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/091dceb95663/sensors-21-00754-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/6960f055331d/sensors-21-00754-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/f62cdf4c7f2b/sensors-21-00754-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/7fb90d33aaf8/sensors-21-00754-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/807be3c61d12/sensors-21-00754-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/453d7abdfd13/sensors-21-00754-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/e70e69c0d6d9/sensors-21-00754-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/80258495ac2a/sensors-21-00754-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/6afe995eade6/sensors-21-00754-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/ab0d1e6eccd4/sensors-21-00754-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/c0059ff99911/sensors-21-00754-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b061/7865562/b959db9a7919/sensors-21-00754-g013.jpg

相似文献

1
A Low-Cost IoT Sensors Network for Monitoring Three-Phase Induction Motor Mechanical Power Adopting an Indirect Measuring Method.一种采用间接测量方法监测三相感应电动机机械功率的低成本物联网传感器网络。
Sensors (Basel). 2021 Jan 23;21(3):754. doi: 10.3390/s21030754.
2
Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things.面向工业物联网的低功耗分布式数据流异常监测技术
Sensors (Basel). 2019 Jun 22;19(12):2804. doi: 10.3390/s19122804.
3
Detecting industrial motor faults with current signatures.用电流特征检测工业电机故障。
F1000Res. 2021 Sep 8;10:903. doi: 10.12688/f1000research.54266.1. eCollection 2021.
4
An IoT Reader for Wireless Passive Electromagnetic Sensors.用于无线无源电磁传感器的物联网读取器。
Sensors (Basel). 2017 Mar 28;17(4):693. doi: 10.3390/s17040693.
5
Performance estimation of three-phase induction motors from no-load startup test without speed acquisition.无速度采集情况下基于空载启动试验的三相感应电动机性能评估
ISA Trans. 2020 Jan;96:376-389. doi: 10.1016/j.isatra.2019.05.028. Epub 2019 Jun 6.
6
Design and Implementation of a Pressure Monitoring System Based on IoT for Water Supply Networks.基于物联网的供水管网压力监测系统的设计与实现。
Sensors (Basel). 2020 Jul 30;20(15):4247. doi: 10.3390/s20154247.
7
Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support.基于拉普拉斯得分的物联网系统特征降维及其在农业监测与决策支持中的应用。
Sensors (Basel). 2020 Sep 8;20(18):5107. doi: 10.3390/s20185107.
8
Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development.基于边缘计算的物联网架构用于低成本空气污染监测系统:全面系统分析、设计考虑因素与开发。
Sensors (Basel). 2018 Sep 10;18(9):3021. doi: 10.3390/s18093021.
9
Smart Sensing Period for Efficient Energy Consumption in IoT Network.物联网网络中高效能耗的智能感知周期。
Sensors (Basel). 2019 Nov 12;19(22):4915. doi: 10.3390/s19224915.
10
Low-Power and Low-Cost Environmental IoT Electronic Nose Using Initial Action Period Measurements.基于初始作用期测量的低功耗低成本环境物联网电子鼻
Sensors (Basel). 2019 Jul 19;19(14):3183. doi: 10.3390/s19143183.

引用本文的文献

1
Impact of Measurement Uncertainty on Fault Diagnosis Systems: A Case Study on Electrical Faults in Induction Motors.测量不确定度对故障诊断系统的影响:以感应电机电气故障为例的研究
Sensors (Basel). 2024 Aug 14;24(16):5263. doi: 10.3390/s24165263.