Development and implementation of medium-fidelity physics-based model for hybrid digital twin-based damage identification of piping structures

IF 3.6 2区 工程技术 Q1 ENGINEERING, CIVIL Journal of Civil Structural Health Monitoring Pub Date : 2024-09-15 DOI:10.1007/s13349-024-00856-z
Pei Yi Siow, Bing Zhen Cheah, Zhi Chao Ong, Shin Yee Khoo, Meisam Gordan, Kok-Sing Lim
{"title":"Development and implementation of medium-fidelity physics-based model for hybrid digital twin-based damage identification of piping structures","authors":"Pei Yi Siow, Bing Zhen Cheah, Zhi Chao Ong, Shin Yee Khoo, Meisam Gordan, Kok-Sing Lim","doi":"10.1007/s13349-024-00856-z","DOIUrl":null,"url":null,"abstract":"<p>Current predictive maintenance technologies are mostly data-driven, where they identify complex relationships using statistics and machine learning (ML) models for damage prediction. The main disadvantage of data-driven or ML models is their high dependency on training data, making them poor in extrapolating and predicting untrained events. Hence, engineers prefer a physics-based model in most cases due to its strong interpretability that aids in supporting critical engineering decisions. However, high-fidelity physics-based models are computationally exhaustive. To preserve the merits and alleviate the inadequacy of both data-driven and physics-based models, recent years have shown an increase in works on hybrid digital twin (DT) models which integrate both methods. This work presents the development of a medium-fidelity physics-based model of a piping structure and its implementation in a hybrid DT for damage identification. Two modelling approaches for the piping support bolted connections were investigated, i.e., bonded contact and spring-based model. The developed physics-based models were correlated with the modal testing data. Results showed that with suitable spring stiffness, the spring-based model has better dynamical representation than the overly stiff bonded contact model with an average natural frequencies deviation below 10% and an average Modal Assurance Criterion (MAC) value of at least 0.75 for both undamaged and damaged conditions. The correlated medium-fidelity spring-based model was used to simulate damage cases for ML training. Results showed that the trained model achieved an accuracy of 95% in identifying the damage at the physical piping structure, thus validating the proposed hybrid DT in damage identification.</p>","PeriodicalId":48582,"journal":{"name":"Journal of Civil Structural Health Monitoring","volume":"53 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Civil Structural Health Monitoring","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s13349-024-00856-z","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0

Abstract

Current predictive maintenance technologies are mostly data-driven, where they identify complex relationships using statistics and machine learning (ML) models for damage prediction. The main disadvantage of data-driven or ML models is their high dependency on training data, making them poor in extrapolating and predicting untrained events. Hence, engineers prefer a physics-based model in most cases due to its strong interpretability that aids in supporting critical engineering decisions. However, high-fidelity physics-based models are computationally exhaustive. To preserve the merits and alleviate the inadequacy of both data-driven and physics-based models, recent years have shown an increase in works on hybrid digital twin (DT) models which integrate both methods. This work presents the development of a medium-fidelity physics-based model of a piping structure and its implementation in a hybrid DT for damage identification. Two modelling approaches for the piping support bolted connections were investigated, i.e., bonded contact and spring-based model. The developed physics-based models were correlated with the modal testing data. Results showed that with suitable spring stiffness, the spring-based model has better dynamical representation than the overly stiff bonded contact model with an average natural frequencies deviation below 10% and an average Modal Assurance Criterion (MAC) value of at least 0.75 for both undamaged and damaged conditions. The correlated medium-fidelity spring-based model was used to simulate damage cases for ML training. Results showed that the trained model achieved an accuracy of 95% in identifying the damage at the physical piping structure, thus validating the proposed hybrid DT in damage identification.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
为基于混合数字孪生的管道结构损伤识别开发和实施中等保真度物理模型
当前的预测性维护技术大多是数据驱动型的,它们利用统计数据和机器学习(ML)模型来识别复杂的关系,从而进行损坏预测。数据驱动或 ML 模型的主要缺点是高度依赖训练数据,因此在推断和预测未经训练的事件方面表现不佳。因此,在大多数情况下,工程师更倾向于使用基于物理的模型,因为它具有很强的可解释性,有助于支持关键的工程决策。然而,基于物理的高保真模型需要耗费大量计算资源。为了保留数据驱动模型和物理模型的优点并缓解其不足,近年来,将这两种方法整合在一起的混合数字孪生(DT)模型的研究越来越多。这项工作介绍了管道结构的中等保真度物理模型的开发及其在混合数字孪生模型中的实施,以进行损伤识别。研究了管道支撑螺栓连接的两种建模方法,即粘接接触模型和基于弹簧的模型。所开发的基于物理的模型与模态测试数据相关联。结果表明,在弹簧刚度合适的情况下,基于弹簧的模型比刚度过大的粘接接触模型具有更好的动态代表性,其平均自然频率偏差低于 10%,在未损坏和已损坏的情况下,平均模态保证标准(MAC)值至少为 0.75。相关的基于弹簧的中等保真度模型用于模拟损伤情况,以进行 ML 训练。结果表明,训练后的模型在识别物理管道结构的损坏方面达到了 95% 的准确率,从而验证了所提出的混合 DT 在损坏识别方面的有效性。
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 去求助
来源期刊
Journal of Civil Structural Health Monitoring
Journal of Civil Structural Health Monitoring Engineering-Safety, Risk, Reliability and Quality
CiteScore
8.10
自引率
11.40%
发文量
105
期刊介绍: The Journal of Civil Structural Health Monitoring (JCSHM) publishes articles to advance the understanding and the application of health monitoring methods for the condition assessment and management of civil infrastructure systems. JCSHM serves as a focal point for sharing knowledge and experience in technologies impacting the discipline of Civionics and Civil Structural Health Monitoring, especially in terms of load capacity ratings and service life estimation.
期刊最新文献
Development and implementation of medium-fidelity physics-based model for hybrid digital twin-based damage identification of piping structures Innovated bridge health diagnosis model using bridge critical frequency ratio R–C–C fusion classifier for automatic damage detection of heritage building using 3D laser scanning An AIoT system for real-time monitoring and forecasting of railway temperature Environmental effects on the experimental modal parameters of masonry buildings: experiences from the Italian Seismic Observatory of Structures (OSS) network
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1