数字孪生、基于物理的模型和机器学习应用于结构损伤检测
Digital twin, physics-based model, and machine learning applied to damage detection in structures
摘要：
这项工作是感兴趣的数字双胞胎，并为他们开发一个简化的框架，在动力系统的背景下。数字孪生是一个巧妙的概念，有助于组织不同领域的专业知识，旨在支持与特定资产相关的工程决策;它阐明了计算模型，传感器，学习，实时分析，诊断，预后等。在这个框架中，并利用其能力，我们探索基于物理的模型与机器学习的集成。数字孪生被构造为一个损坏的结构，其中基于离散物理的计算模型被用来调查几个损坏的情况。机器学习分类器作为数字孪生模型，使用从随机计算模型中获取的数据进行训练。这种策略允许使用可解释的模型（基于物理）来构建一个快速的数字孪生模型（机器学习），该模型将连接到物理孪生模型，以支持实时工程决策。不同的分类器（二次判别，支持向量机等）进行了测试，不同的模型参数（传感器的数量，噪声水平，损伤强度，不确定性，操作参数等）被认为是构建数据集的训练。数字孪生的准确性取决于所分析的场景。通过所选的应用程序，我们能够强调数字孪生模型构建的每一步，包括将基于物理的模型与机器学习相结合的可能性。所探讨的不同情景得出的结论可能有助于广泛的应用。
英文摘要：
This work is interested in digital twins, and the development of a simplified framework for them, in the context of dynamical systems. Digital twin is an ingenious concept that helps on organizing different areas of expertise aiming at supporting engineering decisions related to a specific asset; it articulates computational models, sensors, learning, real time analysis, diagnosis, prognosis, and so on. In this framework, and to leverage its capacity, we explore the integration of physics-based models with machine learning. A digital twin is constructed for a damaged structure, where a discrete physics-based computational model is employed to investigate several damage scenarios. A machine learning classifier, that serves as the digital twin, is trained with data taken from a stochastic computational model. This strategy allows the use of an interpretable model (physics-based) to build a fast digital twin (machine learning) that will be connected to the physical twin to support real time engineering decisions. Different classifiers (quadratic discriminant, support vector machines, etc) are tested, and different model parameters (number of sensors, level of noise, damage intensity, uncertainty, operational parameters, etc) are considered to construct datasets for the training. The accuracy of the digital twin depends on the scenario analyzed. Through the chosen application, we are able to emphasize each step of a digital twin construction, including the possibility of integrating physics-based models with machine learning. The different scenarios explored yield conclusions that might be helpful for a large range of applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
