Active learning for semi-supervised structural health monitoring. (22nd December 2018)

• Record Type: Journal Article
• Authors: Bull, L.; Worden, K.; Manson, G.; Dervilis, N.
• Is Part Of: Journal of sound and vibration. Volume 437(2018)
• Page Start: 373
• Page End: 388
• Publication Date: 2018-12-22
• Subjects: Structural health monitoring -- Vibration monitoring -- Active learning -- Semi-supervised learning -- Classification -- Pattern recognition; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2018.08.040
• Languages: English
• ISSNs: 0022-460X

Abstract

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 .