Real-time OEE visualisation for downtime detection

Yuan Li, Luiz Cesar Gualberto Veras Inoue, R. Sinha
{"title":"Real-time OEE visualisation for downtime detection","authors":"Yuan Li, Luiz Cesar Gualberto Veras Inoue, R. Sinha","doi":"10.1109/INDIN51773.2022.9976067","DOIUrl":null,"url":null,"abstract":"Unknown and unplanned downtime events during production cause significant disruption and loss of productivity. Investigating, identifying and addressing such events is a pressing need. The primary objective of this study is to examine downtime, performance loss, and quality control in the manufacturing process. Specifically, we propose a solution that provides real-time data processing and visualization of the factory floor. This solution was implemented for a major food manufacturer based in New Zealand. The company provided historical data covering over six years of operation and access to real-time data through their Industrial Internet of Things (IIoT) systems executing on Programmable Logic Controllers (PLCs). Our solution is an Overall Equipment Effectiveness (OEE) standardized Supervisory Control and Data Acquisition (SCADA) system that visualizes the manufacturing process in real-time. Analysis of the data collected during this research shows that by implementing the OEE and employing shift adjustment, there was a significant increase in production output. OEE can help improve manufacturing performance by pinpointing the root of the loss of performance in all areas monitored.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN51773.2022.9976067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Unknown and unplanned downtime events during production cause significant disruption and loss of productivity. Investigating, identifying and addressing such events is a pressing need. The primary objective of this study is to examine downtime, performance loss, and quality control in the manufacturing process. Specifically, we propose a solution that provides real-time data processing and visualization of the factory floor. This solution was implemented for a major food manufacturer based in New Zealand. The company provided historical data covering over six years of operation and access to real-time data through their Industrial Internet of Things (IIoT) systems executing on Programmable Logic Controllers (PLCs). Our solution is an Overall Equipment Effectiveness (OEE) standardized Supervisory Control and Data Acquisition (SCADA) system that visualizes the manufacturing process in real-time. Analysis of the data collected during this research shows that by implementing the OEE and employing shift adjustment, there was a significant increase in production output. OEE can help improve manufacturing performance by pinpointing the root of the loss of performance in all areas monitored.
用于停机检测的实时OEE可视化
生产过程中未知和计划外的停机事件会导致严重的中断和生产力损失。调查、查明和处理这类事件是一项迫切需要。本研究的主要目的是研究制造过程中的停机时间、性能损失和质量控制。具体来说,我们提出了一个提供实时数据处理和工厂车间可视化的解决方案。该解决方案是为新西兰的一家主要食品制造商实施的。该公司提供了超过六年的运营历史数据,并通过在可编程逻辑控制器(plc)上执行的工业物联网(IIoT)系统访问实时数据。我们的解决方案是一个整体设备效率(OEE)标准化的监控和数据采集(SCADA)系统,可以实时可视化制造过程。本研究收集的数据分析表明,通过实施OEE和采用班次调整,生产产量显著增加。OEE可以通过在所有被监控的领域中精确定位性能损失的根源来帮助提高制造性能。
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
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