Abstract
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.
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References
Bihsop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Dasgupta, S.: Two faces of active learning. Theor. Comput. Sci. 412(19), 1767–1781 (2011)
Dasgupta, S., Hsu, D.: Hierarchical sampling for active learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 208–215. ACM (2008)
Hofmann, T., Buhmann, J.M.: Active data clustering. In: Advances in Neural Information Processing Systems, pp. 528–534 (1998)
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT Press, Cambridge (2012)
Settles, B.: Active learning literature survey. Univ. Wis. Madison 52(55–66), 11 (2010)
Acknowledgements
The authors gratefully acknowledge the support of the UK Engineering and Physical Sciences Research Council (EPSRC) through Grant reference number EP/J016942/1.
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© 2019 The Society for Experimental Mechanics, Inc.
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Bull, L., Manson, G., Worden, K., Dervilis, N. (2019). Active Learning Approaches to Structural Health Monitoring. In: Dervilis, N. (eds) Special Topics in Structural Dynamics, Volume 5. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-75390-4_14
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DOI: https://doi.org/10.1007/978-3-319-75390-4_14
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