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
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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.

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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。
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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.
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