Damage detection in mechanical structures using extreme value statistic.

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The first and most important objective of any damage identification algorithms 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 ... continued below

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11 p.

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Worden, K.; Allen, D. W. (David W.); Sohn, H. (Hoon) & Farrar, C. R. (Charles R.) January 1, 2002.

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Description

The first and most important objective of any damage identification algorithms 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 weighs 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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11 p.

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  • Submitted to: SPIE's 9th Annual International Symposium on Non Destructive Evaluation, San Diego, CA, March 17-21, 2002

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  • Report No.: LA-UR-02-1891
  • Grant Number: none
  • Office of Scientific & Technical Information Report Number: 976144
  • Archival Resource Key: ark:/67531/metadc933661

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  • January 1, 2002

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 12, 2016, 6:27 p.m.

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Worden, K.; Allen, D. W. (David W.); Sohn, H. (Hoon) & Farrar, C. R. (Charles R.). Damage detection in mechanical structures using extreme value statistic., article, January 1, 2002; United States. (digital.library.unt.edu/ark:/67531/metadc933661/: accessed August 16, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.