Evaporating temperature estimation of refrigeration systems based on vibration data-driven soft sensors

IF 3.5 2区 工程技术 Q1 ENGINEERING, MECHANICAL
Ahryman Seixas Busse de Siqueira Nascimento , João Paulo Zomer Machado , Leandro dos Santos Coelho , Rodolfo César Costa Flesch
{"title":"Evaporating temperature estimation of refrigeration systems based on vibration data-driven soft sensors","authors":"Ahryman Seixas Busse de Siqueira Nascimento ,&nbsp;João Paulo Zomer Machado ,&nbsp;Leandro dos Santos Coelho ,&nbsp;Rodolfo César Costa Flesch","doi":"10.1016/j.ijrefrig.2024.08.020","DOIUrl":null,"url":null,"abstract":"<div><div>The evaluation of the operating conditions of refrigeration compressors once installed in household appliances is challenging due to the need to install pressure transducers, a process which requires system evacuation and refrigerant reintroduction. In addition, changes in the piping modify the characteristics of the original product. This paper proposes a soft-sensing technique based on vibration measurements of the compressor surface to predict the evaporating temperature. Different machine learning (ML) techniques are evaluated as data-driven prediction models, namely multilayer perceptron (MLP) neural networks, least squares boosting, generalized additive model, random forest, extreme learning machine, and random vector functional link neural networks. These techniques were applied to data obtained from a test rig designed to emulate compressor operation in a refrigeration system, with an operating envelope from -30<!--> <!-->°C to -10<!--> <!-->°C for the evaporating temperature and from 34<!--> <!-->°C to 54<!--> <!-->°C for the condensing temperature. The results showed that, with a single vibration measurement point, it was possible to use an MLP technique to estimate the evaporating temperature with a root mean squared error of 1.74<!--> <!-->°C in a non-intrusive way. For the other prediction techniques, the errors were a bit higher than for the MLP, but the maximum error value was about 2.5<!--> <!-->°C in all cases.</div></div>","PeriodicalId":14274,"journal":{"name":"International Journal of Refrigeration-revue Internationale Du Froid","volume":"168 ","pages":"Pages 288-296"},"PeriodicalIF":3.5000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Refrigeration-revue Internationale Du Froid","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0140700724002950","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0

Abstract

The evaluation of the operating conditions of refrigeration compressors once installed in household appliances is challenging due to the need to install pressure transducers, a process which requires system evacuation and refrigerant reintroduction. In addition, changes in the piping modify the characteristics of the original product. This paper proposes a soft-sensing technique based on vibration measurements of the compressor surface to predict the evaporating temperature. Different machine learning (ML) techniques are evaluated as data-driven prediction models, namely multilayer perceptron (MLP) neural networks, least squares boosting, generalized additive model, random forest, extreme learning machine, and random vector functional link neural networks. These techniques were applied to data obtained from a test rig designed to emulate compressor operation in a refrigeration system, with an operating envelope from -30 °C to -10 °C for the evaporating temperature and from 34 °C to 54 °C for the condensing temperature. The results showed that, with a single vibration measurement point, it was possible to use an MLP technique to estimate the evaporating temperature with a root mean squared error of 1.74 °C in a non-intrusive way. For the other prediction techniques, the errors were a bit higher than for the MLP, but the maximum error value was about 2.5 °C in all cases.
基于振动数据驱动的软传感器估算制冷系统的蒸发温度
对安装在家用电器中的制冷压缩机的运行状况进行评估具有挑战性,因为需要安装压力传感器,而这一过程需要系统排空并重新引入制冷剂。此外,管道的变化也会改变原始产品的特性。本文提出了一种基于压缩机表面振动测量的软传感技术,用于预测蒸发温度。作为数据驱动的预测模型,评估了不同的机器学习(ML)技术,即多层感知器(MLP)神经网络、最小二乘提升、广义加法模型、随机森林、极端学习机和随机向量功能链接神经网络。这些技术被应用于从模拟制冷系统压缩机运行的试验台获得的数据,蒸发温度的工作范围为 -30 °C 至 -10 °C,冷凝温度的工作范围为 34 °C 至 54 °C。结果表明,通过单个振动测量点,可以使用 MLP 技术以非侵入方式估算蒸发温度,均方根误差为 1.74 °C。其他预测技术的误差略高于 MLP,但在所有情况下,最大误差值约为 2.5 °C。
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
CiteScore
7.30
自引率
12.80%
发文量
363
审稿时长
3.7 months
期刊介绍: The International Journal of Refrigeration is published for the International Institute of Refrigeration (IIR) by Elsevier. It is essential reading for all those wishing to keep abreast of research and industrial news in refrigeration, air conditioning and associated fields. This is particularly important in these times of rapid introduction of alternative refrigerants and the emergence of new technology. The journal has published special issues on alternative refrigerants and novel topics in the field of boiling, condensation, heat pumps, food refrigeration, carbon dioxide, ammonia, hydrocarbons, magnetic refrigeration at room temperature, sorptive cooling, phase change materials and slurries, ejector technology, compressors, and solar cooling. As well as original research papers the International Journal of Refrigeration also includes review articles, papers presented at IIR conferences, short reports and letters describing preliminary results and experimental details, and letters to the Editor on recent areas of discussion and controversy. Other features include forthcoming events, conference reports and book reviews. Papers are published in either English or French with the IIR news section in both languages.
×
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