Article ID Journal Published Year Pages File Type
10225445 Journal of Sound and Vibration 2018 35 Pages PDF
Abstract
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.
Related Topics
Physical Sciences and Engineering Engineering Civil and Structural Engineering
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