The first and most important objective of any damage identification algorithm is to ascertain with confidence if damage is present or not. Many methods have been proposed for damage detection based on ideas of novelty detection founded in pattern recognition and multivariate statistics. The philosophy of novelty detection is simple. Features are first extracted from a baseline system to be monitored, and subsequent data are then compared to see if the new features are outliers, which significantly depart from the rest of population. In damage diagnosis problems, the assumption is that outliers are generated from a damaged condition of the monitored system. This damage classification necessitates the establishment of a decision boundary. Choosing this threshold value is often based on the assumption that the parent distribution of data is Gaussian in nature. While the problem of novelty detection focuses attention on the outlier or extreme values of the data, i.e., those points in the tails of the distribution, the threshold selection using the normality assumption weights the central population of data. Therefore, this normality assumption might impose potentially misleading behavior on damage classification, and is likely to lead the damage diagnosis astray. In this paper, extreme value statistics is integrated with the novelty detection to specifically model the tails of the distribution of interest. Finally, the proposed technique is demonstrated on simulated numerical data and time series data measured from an eight degree-of-freedom spring-mass system.
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March 2005
Technical Papers
Structural Damage Classification Using Extreme Value Statistics
Hoon Sohn,
Hoon Sohn
Engineering Sciences and Applications Division, Weapons Response Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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David W. Allen,
David W. Allen
Engineering Sciences and Applications Division, Weapons Response Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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Keith Worden,
Keith Worden
Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
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Charles R. Farrar
Charles R. Farrar
Engineering Sciences and Applications Division, Weapons Response Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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Hoon Sohn
Engineering Sciences and Applications Division, Weapons Response Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
David W. Allen
Engineering Sciences and Applications Division, Weapons Response Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Keith Worden
Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
Charles R. Farrar
Engineering Sciences and Applications Division, Weapons Response Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Contributed by the Dynamic Systems, Measurement, and Control Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript final revision, December 26, 2003. Review conducted by: F. Ghorbel.
J. Dyn. Sys., Meas., Control. Mar 2005, 127(1): 125-132 (8 pages)
Published Online: May 10, 2005
Article history
Revised:
December 26, 2003
Online:
May 10, 2005
Citation
Sohn , H., Allen, D. W., Worden, K., and Farrar, C. R. (May 10, 2005). "Structural Damage Classification Using Extreme Value Statistics ." ASME. J. Dyn. Sys., Meas., Control. March 2005; 127(1): 125–132. https://doi.org/10.1115/1.1849240
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