Abstract
Machine learning has been widely and successfully used in many Structural Health Monitoring (SHM) applications. However, many machine learning models can only make accurate predictions when the training and test data are measured from the same system; this is because most traditional machine learning methods assume that all the data are drawn from the same distribution. Therefore, to train a robust predictor, it is often required to recollect and label new training data every time when considering a new structure, which can be significantly expensive, and sometimes impossible in the SHM context. In such cases, the idea of transfer learning may be employed, which aims to transfer knowledge between task domains to improve learners. In this paper, a subfield of transfer learning i.e. domain adaptation, is considered, and its utility in SHM applications is briefly investigated.
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References
Farrar, C.R., Worden, K.: Structural Health Monitoring: a Machine Learning Perspective. Wiley, New York (2012)
Worden, K., Dulieu-Barton, J.M.: An overview of intelligent fault detection in systems and structures. Struct. Health Monit. 3, 85–98 (2004)
Worden, K., Manson, G.: The application of machine learning to structural health monitoring. Philos. Trans. R. Soc. Lond. A 365, 515–537 (2007)
Figueiredo, E., Park, G., Farrar, C.R., Worden, K., Figueiras, J.: Machine learning algorithms for damage detection under operational and environmental variability. Struct. Health Monit. 10, 559–572 (2011)
Dervilis, N., Choi, M., Taylor, S.G., Barthorpe, R.J., Park, G., Farrar, C.R., Worden, K.: On damage diagnosis for a wind turbine blade using pattern recognition. J. Sound Vib. 333, 1833–1850 (2014)
Papatheou, E., Dervilis, N., Maguire, E.A., Antoniadou, I., Worden, K.: Population-based SHM: a case study on an offshore wind farm. In: Proceedings of 10th International Workshop on Structural Health Monitoring, Palo Alto, CA (2015)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359 (2010)
Zhang, J., Li, W., Ogunbona, P.: Transfer learning for cross-dataset recognition: a survey (2017). arXiv:1705.04396v2 [cs.CV]
Bull, L., Worden, K., Manson, G., Dervilis, N.: Active learning for semi-supervised structural health monitoring. J. Sound Vib. 437, 373–388 (2018)
Sugiyama, M., Nakajima, S., Kashima, H., Buenau, P.V., Kawanabe, M.: Direct importance estimation with model selection and its application to covariate shift adaptation. In: Advances in Neural Information Processing Systems, pp. 1433–1440 (2008)
Quanz, B., Huan, J., Mishra, M.: Knowledge transfer with low-quality data: a feature extraction issue. IEEE Trans. Knowl. Data Eng. 24, 1789–1802 (2012)
Baktashmotlagh, M., Harandi, M.T., Lovell, B.C., Salzmann, M.: Domain adaptation on the statistical manifold. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2481–2488 (2014)
Borgwardt, K.M., Gretton, A., Rasch, M.J., Kriegel, H.-P., Schölkopf, B., Smola, A.J.: Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22, e49–e57 (2006)
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22, 199–210 (2011)
Pan, S.J., Kwok, J.T., Yang, Q.: Transfer learning via dimensionality reduction. In: AAAI, vol. 8, pp. 677–682 (2008)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: IEEE International Conference on Computer Vision (ICCV), pp. 2200–2207 (2013)
Long, M., Wang, J., Ding, G., Pan, S.J., Yu, P.S.: Adaptation regularization: A general framework for transfer learning. IEEE Trans. Knowl. Data Eng. 26, 1076–1089 (2014)
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006)
Acknowledgements
The authors would like to gratefully acknowledge the support of the UK Engineering and Physical Sciences Research Council via grants EP/J016942/1 and EP/K003836/2. KW would also like to thank Lawrence Bull of the University of Sheffield for discussions on the nature of active learning and for commenting on the manuscript.
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Liu, X., Worden, K. (2020). On the Application of Domain Adaptation in SHM. 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_17
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DOI: https://doi.org/10.1007/978-3-030-12243-0_17
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