Novelty Detection in Projected Spaces for Structural Health Monitoring
The aim of Structural Health Monitoring (SHM) is to detect and identify damages in man-made structures such as bridges by monitoring features derived from vibration data. A usual approach is to deal with vibration measurements, obtained by acceleration sensors during the service life of the structure. In this case, only normal data from healthy operation are available, so damage detection becomes a novelty detection problem. However, when prior knowledge about the structure is limited, the set of candidate features that can be extracted from the set of sensors is large and dimensionality reduction of the input space can result in more precise and efficient novelty detectors. We assess the effect of linear, nonlinear, and random projection to low-dimensional spaces in novelty detection by means of probabilistic and nearest-neighbor methods. The methods are assessed with real-life data from a wooden bridge model, where structural damages are simulated with small added weights.
Keywordsnovelty detection dimensionality reduction damage detection structural health monitoring sensor network
Unable to display preview. Download preview PDF.
- 2.Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: Knowledge Discovery and Data Mining (KDD 2001), pp. 245–250 (2001)Google Scholar
- 4.Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection – a survey. ACM Computing Surveys 41(3), 15:1–15:44 (2009)Google Scholar
- 9.Hérault, J., Jausions-Picaud, C., Guérin-Dugué, A.: Curvilinear Component Analysis for High-Dimensional Data Representation: I. Theoretical Aspects and Practical Use in the Presence of Noise. In: Mira, J. (ed.) IWANN 1999. LNCS, vol. 1607, pp. 625–634. Springer, Heidelberg (1999)CrossRefGoogle Scholar
- 11.Kullaa, J.: Elimination of environmental influences from damage-sensitive features in a structural health monitoring system. In: Balageas, D.L. (ed.) Proceedings of the First European Workshop on Structural Health Monitoring 2002, Onera, pp. 742–749. DEStech Publications Inc. (2002)Google Scholar
- 12.Lee, J.A., Verleysen, M.: Nonlinear Dimensionality Reduction. In: Information Science and Statistics. Springer, Heidelberg (2007)Google Scholar
- 17.Tax, D.M.J.: One-class classification; Concept-learning in the absence of counter-examples. Ph.D. thesis, Delft University of Technology (June 2001)Google Scholar
- 18.Tax, D.: DDtools, Data Description Toolbox for Matlab, version 1.7.3 (December 2009)Google Scholar
- 20.Vesanto, J., Alhoniemi, E., Himberg, J., Kiviluoto, K., Parviainen, J.: Self-organizing map for data mining in MATLAB: The SOM toolbox. Simulation News Europe 9(25), 54 (1999)Google Scholar