Active Learning Approaches to Structural Health Monitoring
Active Learning Approaches to Structural Health Monitoring
复制标题
结构健康监测的主动学习方法
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
N. Dervilis
中科院分区：
文献类型：
--
作者：
L. Bull;G. Manson;K. Worden;N. Dervilis
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系统数据的诊断标签。在工程背景下，这些标签的获取成本很高，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数据来查询标签来解决这个问题。本文演示了主动学习的相关性，使用Dasgupta和Hsu（DH主动学习者）提出的算法。结果提供了这种技术的应用程序，从飞机实验的工程数据。
A critical issue for structural health monitoring (SHM) strategies based on pattern recognition models is a lack of diagnostic labels for system data. In an engineering context these 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 data to query for labels. This article demonstrates the relevance of active learning, using the algorithm proposed by Dasgupta and Hsu (the DH active learner). Results are provided for applications of this technique to engineering data from aircraft experiments.