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Towards Physics-Based Metrics for Transfer Learning in Dynamics

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Data Science in Engineering, Volume 10 (SEM 2023)

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Abstract

Data used for training structural health monitoring (SHM) systems are often expensive and/or impractical to obtain, particularly labelled data. Population-based SHM presents a potential solution to this issue by considering the available data across a population of structures. However, differences between structures will mean that the training and testing distributions will differ; thus, conventional machine learning methods cannot be expected to generalise between structures. To address this issue, transfer learning—in the form of domain adaptation (DA)—can be used to align the underlying distributions. An important consideration when applying DA is that it may lead to performance degradation; this scenario is referred to as negative transfer. Furthermore, validating whether negative transfer has occurred is challenging without labelled data, so assessing the similarity between the datasets is of critical importance to mitigate the likelihood of negative transfer. Typical unsupervised metrics measure the discrepancy between the marginal distributions, so they are not robust to scenarios where the conditional distributions differ significantly. This paper presents a discussion on negative transfer, proposing a physics-based metric that utilises the modal assurance criteria (MAC), between the modes of the healthy structures. This metric is compared to other popular metrics, showing that the proposed MAC-based metric indicates the likelihood of negative transfer under conditional distribution shift, whereas the previous unsupervised methods cannot.

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Notes

  1. 1.

    This method assumes that some normal condition data can be labelled by assuming data from the start of a monitoring campaign correspond to the healthy structure.

  2. 2.

    This approach could be used to predict any performance metric by normalising values to \([0,1]\).

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Acknowledgements

The authors would like to acknowledge the support of the UK Engineering and Physical Sciences Research Council via grants EP/R006768/1, EP/R003645/1, EP/R004900/1 and EP/W005816/1. For the purpose of open access, the authors have applied for a Creative Commons Attribution (CC-BY-ND) licence to any author accepted manuscript version arising.

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Authors and Affiliations

  1. Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK

    J. Poole, P. Gardner, N. Dervilis, J. H. Mclean, T. J. Rogers & K. Worden

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  1. J. Poole
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  2. P. Gardner
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  3. N. Dervilis
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  4. J. H. Mclean
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  5. T. J. Rogers
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  6. K. Worden
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Correspondence to J. Poole .

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Editors and Affiliations

  1. Kount, an Equifax company, Boise, ID, USA

    Ramin Madarshahian

  2. Kount, an Equifax company, Oakland, CA, USA

    François Hemez

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Poole, J., Gardner, P., Dervilis, N., Mclean, J.H., Rogers, T.J., Worden, K. (2023). Towards Physics-Based Metrics for Transfer Learning in Dynamics. In: Madarshahian, R., Hemez, F. (eds) Data Science in Engineering, Volume 10. SEM 2023. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-031-34946-1_9

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