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首页> 外文期刊>Mechanical systems and signal processing >Foundations of population-based SHM, Part Ⅲ: Heterogeneous populations - Mapping and transfer
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Foundations of population-based SHM, Part Ⅲ: Heterogeneous populations - Mapping and transfer

机译：基于人口的SHM的基础，第Ⅲ部分：异构人群 - 绘图和转移

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摘要

This is the third and final paper in a series laying foundations for a theory/methodology of Population-Based Structural Health Monitoring (PBSHM). PBSHM involves utilising knowledge from one set of structures in a population and applying it to a different set, such that predictions about the health states of each member in the population can be performed and improved. Central ideas behind PBSHM are those of knowledge transfer and mapping. In the context of PBSHM, knowledge transfer involves using information from a source domain structure, where labels are known for given feature sets, and mapping these onto the unlabelled feature space of a different, target domain structure. This mapping means a classifier trained on the transformed source domain data will generalise to the unlabelled target domain data; i.e. a classifier built on one structure will generalise to another, making Structural Heath Monitoring (SHM) cost-effective and applicable to a wide range of challenging industrial scenarios. This process of mapping features and labels across source and target domains is defined here via domain adaptation, a subcategory of transfer learning. A mathematical underpinning for when domain adaptation is possible in a structural dynamics context is provided, with reference to topology within a graphical representation of structures. Subsequently, a novel procedure for performing domain adaptation on topo-logically different structures is outlined.
机译：这是一个基于人口的结构健康监测（PBSHM）的理论/方法的系列铺设基础中的第三个和最后一篇论文。 PBSHM涉及利用人口中的一组结构的知识并将其应用于不同的集合，使得可以执行关于人群中每个成员的健康状态的预测。 PBSHM背后的中央观点是知识转移和映射。在PBSHM的上下文中，知识传输涉及使用来自源域结构的信息，其中标签已知给定的特征集，并将其映射到不同的目标域结构的未标记的特征空间上。该映射意味着在变换的源域数据上训练的分类器将概括为未标记的目标域数据;即，在一个结构上构建的分类器将概括为另一个结构，使结构性Heath监测（SHM）具有成本效益，适用于各种挑战性的工业场景。通过域自适应，转移学习子类别定义了跨源域和目标域的映射功能和标签的此过程。在结构动态上下文中提供了在结构动态上下文中可以参考结构的图形表示中的拓扑时，提供了一个数学的基础。随后，概述了用于在逻辑上不同的结构上执行域自适应的新方法。

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  • 来源
    《Mechanical systems and signal processing》 |2021年第2期|107142.1-107142.29|共29页
  • 作者

    P. Gardner; L.A. Bull; J. Gosliga; N. Dervilis; K. Worden;

  • 作者单位

    Dynamics Research Croup Department of Mechanical Engineering University of Sheffield Mappin Street Sheffield S1 3JD UK;

    Dynamics Research Croup Department of Mechanical Engineering University of Sheffield Mappin Street Sheffield S1 3JD UK;

    Dynamics Research Croup Department of Mechanical Engineering University of Sheffield Mappin Street Sheffield S1 3JD UK;

    Dynamics Research Croup Department of Mechanical Engineering University of Sheffield Mappin Street Sheffield S1 3JD UK;

    Dynamics Research Croup Department of Mechanical Engineering University of Sheffield Mappin Street Sheffield S1 3JD UK;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Population-based structural health; monitoring; Transfer learning; Domain adaptation;

    机译：基于人口的结构健康;监测;转移学习;域适应;

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