Comparative Study of Robust Novelty Detection Techniques

Abstract:

The central target of this work is to provide an alternative to machine learning approaches to structural health monitoring with one of robust multivariate statistic novelty detection. Damage detection and identification is a procedure that is hierarchical in nature. At its most sophisticated, diagnosis of the damage could include localisation, classification and severity assessment and even go so far as to estimate the time-to-failure of the structure. In this paper, robust multivariate statistics were investigated focused mainly on a high level estimation of the outliers which determines only the presence or absence of novelty - something that is of fundamental interest. These methods allow a diagnosis of deviation from normality and the option of identifying the presence of masking effects caused by multiple outliers. This paper is trying to introduce a new scheme for damage detection by adopting simple measurements and exploiting robust multivariate statistics.

• Periodical: Key Engineering Materials (Volumes 569-570)
• Pages: 1109-1115
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.569-570.1109
• Online since: July 2013
• Authors: Nikolaos Dervilis, Robert J. Barthorpe, Keith Worden
• Keywords: Damage Detection, Minimum Volume Enclosing Ellipsoid, Novelty Detection, Robust Outlier Analysis

References

[1] K. Worden, Structural fault detection using a novelty measure, Journal of Sound and Vibration, 201(1), pp.85-101, (1997).

DOI: 10.1006/jsvi.1996.0747

[2] K. Worden, G. Manson, D. J. Allman, Experimental validation of a structural health monitoring methodology: Part I. Novelty detection on a laboratory structure, Journal of Sound and Vibration, 259, 323–343, (2003).

DOI: 10.1006/jsvi.2002.5168

[3] K. Worden, G. Manson, N. R. Fieller, Damage detection using outlier analysis, Journal of Sound and Vibration, 229, 647–667, (1999).

DOI: 10.1006/jsvi.1999.2514

[4] G. Manson, K. Worden, D. J. Allman. Experimental validation of a structural health monitoring methodology: Part III. Novelty detection on an aircraft wing, Journal of Sound and Vibration, 259 , 345–363, (2003).

DOI: 10.1006/jsvi.2002.5167

[5] G. Manson, K. Worden, D. J. Allman, Experimental validation of a structural health monitoring methodology: Part II. Novelty detection on an aircraft wing, Journal of Sound and Vibration. 259, 345–363, (2003).

DOI: 10.1006/jsvi.2002.5167

[6] L. G. Khachiyan, Rounding of polytopes in the real number model of computation, Mathematics of Operations Research 21 (1996), 307-320.

DOI: 10.1287/moor.21.2.307

[7] P. Kumar and E. A. Yildirim, Mimimum volume enclosing ellipsoids and core sets, (2005).

[8] S. Peng, and R.M. Freund. Computation of minimum-volume covering ellipsoids., Operations Research (2004): 690-706.

DOI: 10.1287/opre.1040.0115

[9] N. Moshtagh, Minimum Volume Enclosing Ellipsoids (unpublished paper), University of Pennsylvania, Philadelphia, PA, USA.

[10] P. J. Rousseeuw, and A. M. Leroy, (1987). Robust Regression and Outlier Detection. Wiley-Interscience, New York.

[11] P. J. Rousseeuw and K. Van Driessen, (1999). A fast algorithm for the minimum covariance determinant estimator. Technometrics 41 212–223.

DOI: 10.1080/00401706.1999.10485670

[12] S. Verboven, and M. Hubert, (2005). LIBRA: a MATLAB library for robust analysis. Chemometrics and Intelligent Laboratory Systems, 75(2), 127-136.

DOI: 10.1016/j.chemolab.2004.06.003

[13] M. Hubert, P. J. Rousseeuw and Stefan Van Aelst, High-Breakdown Robust Multivariate Methods, Statistical Science 2008, Vol. 23, No. 1, 92–119, Institute of Mathematical Statistics, (2008).

DOI: 10.1214/088342307000000087

[14] P. J. Rousseeuw and B. C. van Zomeren, (1990), Unmasking multivariate outliers and leverage points. J. Amer. Statist. Assoc. 85 633–651.

DOI: 10.1080/01621459.1990.10474920

[15] D. A. Jackson and Yong Chen, Robust principal component analysis and outlier detection with ecological data, Environmetrics Environmetrics 2004; 15: 129–139.

DOI: 10.1002/env.628

[16] C. M. Bishop, Neural networks for pattern recognition, Oxford University Press, (1995).

[17] C. M. Bishop, Pattern recognition and machine learning, Springer Press, (2006).