Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10225445 | Journal of Sound and Vibration | 2018 | 35 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Engineering
Civil and Structural Engineering
Authors
L. Bull, K. Worden, G. Manson, N. Dervilis,