Foundations of Population-based SHM, Part II: Heterogeneous populations - Graphs, networks, and communities
Foundations of Population-based SHM, Part II: Heterogeneous populations - Graphs, networks, and communities
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DOI：
10.1016/j.ymssp.2020.107144
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发表时间：
2021-02-01
影响因子：
8.4
通讯作者：
Worden, K.
中科院分区：
文献类型：
--
作者：
Gosliga, J.;Gardner, P. A.;Worden, K.
本文是旨在为基于群体的结构健康监测(PBSHM)提供基础的三篇系列文章中的第二篇；PBSHM是一种新技术，将允许跨结构群体传输诊断信息，增强SHM能力，使其超越适用于单个结构的能力。新的PBSHM可能允许有关正常运行条件、损坏状态的知识，甚至是基于物理的模型在结构之间传输。本系列的第一部分讨论了具有名义上相同结构的同质种群。该理论在本文中被扩展到具有不同结构的异质种群。为了达到这一目的，提出了一种基于不可约元素(IE)模型的结构抽象表示方法，该模型能够捕捉结构的本质特征，然后将这些特征转换为属性图(AG)。AG形成了一个复杂的结构模型网络，在该网络上可以使用一个度量来评估结构相似性；相似性是诊断信息能否成功传递的关键度量。一旦在结构网络上建立了成对相似性度量，相似的结构就被聚类以形成社区。在这些社区中，人们假定可以进行一定程度的知识转移。传输本身将使用机器学习方法来完成，这将在本系列的第三部分中讨论。本文介绍的思想可以用来定义同质和异质人口情况下PBSHM的精确术语。(C)2020年提交人(S)。爱思唯尔有限公司出版。
This paper is the second in a series of three which aims to provide a basis for Population-Based Structural Health Monitoring (PBSHM); a new technology which will allow transfer of diagnostic information across a population of structures, augmenting SHM capability beyond that applicable to individual structures. The new PBSHM can potentially allow knowledge about normal operating conditions, damage states, and even physics-based models to be transferred between structures. The first part in this series considered homogeneous populations of nominally-identical structures. The theory is extended in this paper to heterogeneous populations of disparate structures. In order to achieve this aim, the paper introduces an abstract representation of structures based on Irreducible Element (IE) models, which capture essential structural characteristics, which are then converted into Attributed Graphs (AGs). The AGs form a complex network of structure models, on which a metric can be used to assess structural similarity; the similarity being a key measure of whether diagnostic information can be successfully transferred. Once a pairwise similarity metric has been established on the network of structures, similar structures are clustered to form communities. Within these communities, it is assumed that a certain level of knowledge transfer is possible. The transfer itself will be accomplished using machine learning methods which will be discussed in the third part of this series. The ideas introduced in this paper can be used to define precise terminology for PBSHM in both the homogeneous and heterogeneous population cases. (C) 2020 The Author(s). Published by Elsevier Ltd.