智能建筑中基于云的协同故障检测与诊断框架

S. Lazarova-Molnar, N. Mohamed
{"title":"智能建筑中基于云的协同故障检测与诊断框架","authors":"S. Lazarova-Molnar, N. Mohamed","doi":"10.1109/ICMSAO.2017.7934905","DOIUrl":null,"url":null,"abstract":"The potential for saving on energy related cost with timely and accurate Fault Detection and Diagnosis (FDD) in Building Management Systems (BMS) has been estimated to ca. 15–30% of the overall building energy-related cost. Due to the expansion of well-equipped smart buildings that feature multitudes of sensors and meters that enable collection of large amounts of data, FDD data-based methods have become very popular. Sensor and meter data, however, have been found as insufficient for FDD purposes, and it needs to be complemented with event/fault data that is more difficult to obtain. To account for the unavailability of event/fault data, BMS can benefit from sharing each other's data and utilize it for collaborative FDD. To support collaborative FDD and sharing of data, we rely on cloud computing. In this paper we present a framework for collaborative FDD of smart buildings that utilizes cloud computing and enables BMS to share and benefit from each other's data.","PeriodicalId":265345,"journal":{"name":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"A framework for collaborative cloud-based fault detection and diagnosis in smart buildings\",\"authors\":\"S. Lazarova-Molnar, N. Mohamed\",\"doi\":\"10.1109/ICMSAO.2017.7934905\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The potential for saving on energy related cost with timely and accurate Fault Detection and Diagnosis (FDD) in Building Management Systems (BMS) has been estimated to ca. 15–30% of the overall building energy-related cost. Due to the expansion of well-equipped smart buildings that feature multitudes of sensors and meters that enable collection of large amounts of data, FDD data-based methods have become very popular. Sensor and meter data, however, have been found as insufficient for FDD purposes, and it needs to be complemented with event/fault data that is more difficult to obtain. To account for the unavailability of event/fault data, BMS can benefit from sharing each other's data and utilize it for collaborative FDD. To support collaborative FDD and sharing of data, we rely on cloud computing. In this paper we present a framework for collaborative FDD of smart buildings that utilizes cloud computing and enables BMS to share and benefit from each other's data.\",\"PeriodicalId\":265345,\"journal\":{\"name\":\"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMSAO.2017.7934905\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMSAO.2017.7934905","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

摘要

据估计,楼宇管理系统若能及时及准确地侦测及诊断楼宇故障,可节省约15%至30%的楼宇能源成本。由于设备齐全的智能建筑的扩展,具有大量传感器和仪表,可以收集大量数据,基于FDD数据的方法已经变得非常流行。然而,已经发现传感器和仪表数据不足以用于FDD目的,并且需要用更难获得的事件/故障数据进行补充。为了解释事件/故障数据的不可用性,BMS可以从共享彼此的数据中获益,并将其用于协作FDD。为了支持协作式FDD和数据共享,我们依靠云计算。在本文中,我们提出了一个利用云计算的智能建筑协同FDD框架,使BMS能够共享彼此的数据并从中受益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A framework for collaborative cloud-based fault detection and diagnosis in smart buildings
The potential for saving on energy related cost with timely and accurate Fault Detection and Diagnosis (FDD) in Building Management Systems (BMS) has been estimated to ca. 15–30% of the overall building energy-related cost. Due to the expansion of well-equipped smart buildings that feature multitudes of sensors and meters that enable collection of large amounts of data, FDD data-based methods have become very popular. Sensor and meter data, however, have been found as insufficient for FDD purposes, and it needs to be complemented with event/fault data that is more difficult to obtain. To account for the unavailability of event/fault data, BMS can benefit from sharing each other's data and utilize it for collaborative FDD. To support collaborative FDD and sharing of data, we rely on cloud computing. In this paper we present a framework for collaborative FDD of smart buildings that utilizes cloud computing and enables BMS to share and benefit from each other's data.
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