Development of an ontology-based asset information model for predictive maintenance in building facilities

IF 3.5 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
Diego Espinosa Gispert, I. Yitmen, Habib Sadri, Afshin Taheri
{"title":"Development of an ontology-based asset information model for predictive maintenance in building facilities","authors":"Diego Espinosa Gispert, I. Yitmen, Habib Sadri, Afshin Taheri","doi":"10.1108/sasbe-07-2023-0170","DOIUrl":null,"url":null,"abstract":"PurposeThe purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.Design/methodology/approachA scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.FindingsThe research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.Practical implicationsThe proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.Originality/valueThe research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.","PeriodicalId":45779,"journal":{"name":"Smart and Sustainable Built Environment","volume":"23 25","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart and Sustainable Built Environment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/sasbe-07-2023-0170","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

PurposeThe purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.Design/methodology/approachA scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.FindingsThe research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.Practical implicationsThe proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.Originality/valueThe research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.
为建筑设施的预测性维护开发基于本体的资产信息模型
本研究的目的是为数字孪生(DT)平台开发一个基于本体的资产信息模型(AIM)框架,并增强建筑设施的预测性维护实践,从而在运维(O&M)过程中实现主动和数据驱动的决策。设计/方法/方法完成了范围界定文献综述,为本研究建立了理论基础。研究了一种基于本体的建筑设施预测性维护模型的开发方法。与行业专业人士进行了半结构化访谈,以收集基于本体的AIM框架验证和见解的定性数据。研究结果表明,虽然本体论的发展面临着在预测性维护背景下定义缺失实体和关系的挑战,但从访谈中获得的见解能够为设施管理(FM)部门采用基于本体论的AIM建立一个全面的框架。实际意义提出的基于本体的AIM有可能在过程中实现主动和数据驱动的决策,优化预测性维护实践,最终提高建筑行业的能源效率和可持续性。原创性/价值本研究为本体开发过程提供了实用指南,并为数字孪生平台提出了基于本体的AIM框架。
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
Smart and Sustainable Built Environment
Smart and Sustainable Built Environment GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
9.20
自引率
8.30%
发文量
53
×
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