Active learning for semi-supervised structural health monitoring
Active learning for semi-supervised structural health monitoring
复制标题
用于半监督结构健康监测的主动学习
DOI：
10.1016/j.jsv.2018.08.040
复制
发表时间：
2018-12-22
影响因子：
4.7
通讯作者：
Dervilis, N.
中科院分区：
文献类型：
--
作者：
Bull, L.;Worden, K.;Dervilis, N.
基于模式识别模型的结构健康监测（SHM）策略的一个关键问题是缺乏诊断标签来解释测量数据。在工程背景下，这些描述性标签的获得成本很高，因此，传统的监督学习是不可行的。主动学习工具希望通过选择有限数量的信息量最大的观察来查询标签来解决这个问题。这项工作提出了集群自适应主动学习的应用，从飞机实验测量数据。这些测试成功地说明了利用主动学习工具进行SHM的优势，并且它们呈现了主动学习方法对工程数据的第一个应用/适应-MATLAB包可通过GitHub获得：https://github.com/labull/cluster_based_active_learning。(C)2018作者爱思唯尔有限公司出版
A critical issue for structural health monitoring (SHM) strategies based on pattern recognition models is a lack of diagnostic labels to explain the measured data. In an engineering context, these descriptive labels are costly to obtain, and as a result, conventional supervised learning is not feasible. Active learning tools look to solve this issue by selecting a limited number of the most informative observations to query for labels. This work presents the application of cluster-adaptive active learning to measured data from aircraft experiments. These tests successfully illustrate the advantages of utilising active learning tools for SHM, and they present the first application/adaptation of active learning methods to engineering data - a MATLAB package is available via GitHub: https://github.com/labull/cluster_based_active_learning. (C) 2018 The Authors. Published by Elsevier Ltd.