Research Papers

Effects of Dimensional Reduction Techniques on Structural Damage Assessment Under Uncertainty

[+] Author and Article Information
Israel Lopez1

 Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA 95616

Nesrin Sarigul-Klijn

 Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA 95616nsarigulklijn@ucdavis.edu


Corresponding author.

J. Vib. Acoust 133(6), 061008 (Oct 18, 2011) (12 pages) doi:10.1115/1.4003592 History: Received December 20, 2008; Revised October 24, 2010; Published October 18, 2011; Online October 18, 2011

In this paper, we present a study of dimensional reduction techniques for structural damage assessment of time-varying structures under uncertainty. Discrete tracking of the frequency response and the mode shape curvature index method is employed to perform damage assessment. Assessment of spontaneous damage in deteriorating structures is important as it can have potential benefits in improving their safety and performance. Most of the available damage assessment techniques incorporate the usage of system identification and classification techniques for detecting damage, location, and/or severity; however, much work is needed in the area of dimensional reduction in order to compress the ever-increasing data and facilitate decision-making in damage assessment classification. A comparison of dimensional reduction techniques is presented and evaluated in terms of separating damaged from undamaged data sets under two types of uncertainty, structural deterioration and environmental uncertainties. The use of a recursive principal component analysis for detecting and tracking structural deterioration and spontaneous damage is evaluated via computational simulations. The results of this study reveal that dimensional reduction techniques can greatly enhance structural damage assessment under uncertainties. This paper compares multiple dimensional reduction techniques by identifying their weaknesses and strengths.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 7

Application of RPCA: (a) principal eigenvalue and (b) principal component

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Figure 8

Case 3 damage index at 1% noise results: (a) without PCA and Z-test and (b) with PCA and Z-test

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Figure 1

Dimensionality reduction methodology in (a) damage detection and (b) damage localization

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Figure 2

Schematic of cantilever beam model

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Figure 3

Structural deterioration (aging) model with discrete damage event

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Figure 4

Principal component of natural frequency feature set for each dimensional reduction method with 2% noise

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Figure 5

Comparison of dimensional reduction methods for each case study based on OA results under the following noise levels: (a) 1.0%, (b) 2.0%, (c) 2.5%, and (d) 3.5%

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Figure 6

Comparison of classification methods for each case study based on OA results under the following noise levels: (a) 1.0%, (b) 2.0%, (c) 2.5%, and (d) 3.5%




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