Novelty Detection in Projected Spaces for Structural Health Monitoring

  • Janne Toivola
  • Miguel A. Prada
  • Jaakko Hollmén
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6065)


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.


novelty detection dimensionality reduction damage detection structural health monitoring sensor network 


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  1. 1.
    Achlioptas, D.: Database-friendly random projections: Johnson-Lindenstrauss with binary coins. Journal of Computer and System Sciences 66(4), 671–687 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 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
  3. 3.
    Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)CrossRefGoogle Scholar
  4. 4.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection – a survey. ACM Computing Surveys 41(3), 15:1–15:44 (2009)Google Scholar
  5. 5.
    Demartines, P., Hérault, J.: Curvilinear component analysis: a self organizing neural network for non linear mapping of data sets. IEEE Transactions on Neural Networks 8, 148–154 (1997)CrossRefGoogle Scholar
  6. 6.
    Deraemaeker, A., Reynders, E., Roeck, G.D., Kullaa, J.: Vibration-based structural health monitoring using output-only measurements under changing environment. Mechanical Systems and Signal Processing 22(1), 34–56 (2008)CrossRefGoogle Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Chichester (2001)zbMATHGoogle Scholar
  8. 8.
    Farrar, C.R., Worden, K.: An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A 365, 303–315 (2007)CrossRefGoogle Scholar
  9. 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
  10. 10.
    Johnson, T.J., Adams, D.E.: Transmissibility as a differential indicator of structural damage. Journal of Vibration and Acoustics 124(4), 634–641 (2002)CrossRefGoogle Scholar
  11. 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. 12.
    Lee, J.A., Verleysen, M.: Nonlinear Dimensionality Reduction. In: Information Science and Statistics. Springer, Heidelberg (2007)Google Scholar
  13. 13.
    Montalvão, D., Maia, N.M.M., Ribeiro, A.M.R.: A Review of Vibration-based Structural Health Monitoring with Special Emphasis on Composite Materials. The Shock and Vibration Digest 38(4), 295–324 (2006)CrossRefGoogle Scholar
  14. 14.
    Parzen, E.: On estimation of a probability density function and mode. The Annals of Mathematical Statistics 33(3), 1065–1076 (1962)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Redner, R., Walker, H.: Mixture densities, maximum likelihood and the EM algorithm. SIAM Review 26(2), 195–234 (1984)zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Swets, J.A.: Measuring the accuracy of diagnostic systems. Science 240(4857), 1285–1293 (1988)CrossRefMathSciNetGoogle Scholar
  17. 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. 18.
    Tax, D.: DDtools, Data Description Toolbox for Matlab, version 1.7.3 (December 2009)Google Scholar
  19. 19.
    Toivola, J., Hollmén, J.: Feature extraction and selection from vibration measurements for structural health monitoring. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 213–224. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 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
  21. 21.
    Worden, K., Farrar, C.R., Manson, G., Park, G.: The fundamental axioms of structural health monitoring. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science 463(2082), 1639–1664 (2007)CrossRefGoogle Scholar
  22. 22.
    Worden, K., Manson, G.: The application of machine learning to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365(1851), 515–537 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Janne Toivola
    • 1
  • Miguel A. Prada
    • 1
  • Jaakko Hollmén
    • 1
  1. 1.Department of Information and Computer ScienceAalto University School of Science and TechnologyAaltoFinland

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