Abstract
A critical issue for data-based engineering is a lack of descriptive labels for the measured data. For many engineering systems, these labels are costly/impractical to obtain, and as a result, conventional supervised learning is not feasible. This article outlines a probabilistic framework for the investigation and labelling of engineering datasets. Two alternative probabilistic measures are suggested to define the most informative observations to investigate and annotate, in order to maximise the classification performance of a statistical model.
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Acknowledgements
The authors gratefully acknowledge the support of the UK Engineering and Physical Sciences Research Council (EPSRC) through Grant reference number EP/R003645/1. Further thanks are extended to Karen Holford and Rhys Pullin at Cardiff University for providing the AE data.
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© 2020 Society for Experimental Mechanics, Inc.
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Bull, L.A., Worden, K., Rogers, T.J., Cross, E.J., Dervilis, N. (2020). Investigating Engineering Data by Probabilistic Measures. In: Dervilis, N. (eds) Special Topics in Structural Dynamics & Experimental Techniques, Volume 5. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-12243-0_12
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DOI: https://doi.org/10.1007/978-3-030-12243-0_12
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