Abstract
In recent decades, novelty detection has attracted considerable attention in the structural health monitoring field. Numerous machine-learning algorithms have been proposed to carry out novelty detection for structural damage detection and localization, owing to their abilities to identify abnormal data in large numbers of datasets. This paper introduces a number of unsupervised novelty detection methods, based on machine learning, and applies them to localize different types of damage in a laboratory scale (lab-scale) structure. The key concept behind unsupervised novelty detection is that the novelty detection model must be trained using only normal data. In this study, the model used to identify abnormal data in the testing datasets has been well trained using normal data. The unsupervised novelty detection methods in this paper include the Gaussian mixture method, one-class support vector machines, and the density peak-based fast clustering method, which was developed recently. To enable these methods to carry out novelty detection and to increase their localization accuracy, a number of improvements have been made to the original algorithms. In this comparative study of structural damage localization, two damage-sensitive features are extracted from the acceleration signals measured by the sensors installed on a complex lab-scale structure. The advantages and disadvantages of these methods are analyzed based on experimental comparative case studies of damage localization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. Wiley, Hoboken, NJ (2012)
Cha, Y.J., Trocha, P., Buyukozturk, O.: Field measurement based system identification and dynamic response prediction of a unique MIT building. Sensors. 16(7), 1016 (2016)
Barthorpe, R.J.: On model-and data-based approaches to structural health monitoring. Ph.D. thesis, University of Sheffield, Sheffield, UK (2010)
Ding, X., Li, Y., Belatreche, A., Maguire, L.P.: An experimental evaluation of novelty detection methods. Neurocomputing. 135, 313–327 (2014)
Walsh, S.B., Borello, D.J., Guldur, B., Hajjar, J.F.: Data processing of point clouds for object detection for structural engineering applications. Comput. Aided Civ. Inf. Eng. 28(7), 495–508 (2013)
Manson, G., Worden, K., Holford, K., Pullin, R.: Visualisation and dimension reduction of acoustic emission data for damage detection. J. Intell. Mater. Syst. Struct. 12(8), 529–536 (2001)
Noh, H.Y., Nair, K.K., Kiremidjian, A.S., Loh, C.H.: Application of time series based damage detection algorithms to the benchmark experiment at the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Smart Struct. Syst. 5(1), 95–117 (2009)
Long, J., Buyukozturk, O.: Automated structural damage detection using one-class machine learning. Dyn. Civil Struct. 4, 117–128 (2014)
Khoa, N.L.D., Zhang, B., Wang, Y., Chen, F., Mustapha, S.: Robust dimensionality reduction and damage detection approaches in structural health monitoring. Struct. Health Monit. 13(4), 406–417 (2014)
Reynolds, D.: Gaussian mixture models. In: Encyclopedia of Biometrics, pp. 827–832. Springer, New York (2015)
Masud, M.M., Gao, J., Khan, L., Han, J., Thuraisingham, B.: Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans. Knowl. Data Eng. 23(6), 859–874 (2011)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science. 344(6191), 1492–1496 (2014)
Benoudjit, N., Archambeau, C., Lendasse, A., Lee, J.A., Verleysen, M.: Width optimization of the Gaussian kernels in radial basis function networks. In: Proceedings of the 10th European Symposium on Artificial Neural Networks, vol. 2, pp. 425–432 (2002)
Cha, Y.J., Buyukoztur, O.: Structural damage detection using modal strain energy and hybrid multiobjective optimization. Comput. Aided Civ. Inf. Eng. 30(5), 347–358 (2015)
Cha, Y.J., Wang, Z.: Unsupervised novelty detection-based structural damage localization using a density peaks-based fast clustering algorithm. Struct. Health Monit. (2017). doi:10.1177/1475921717691260
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 The Society for Experimental Mechanics, Inc.
About this paper
Cite this paper
Wang, Z., Cha, YJ. (2017). Unsupervised Novelty Detection Techniques for Structural Damage Localization: A Comparative Study. In: Barthorpe, R., Platz, R., Lopez, I., Moaveni, B., Papadimitriou, C. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-54858-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-54858-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54857-9
Online ISBN: 978-3-319-54858-6
eBook Packages: EngineeringEngineering (R0)