0
TECHNICAL PAPERS

Time Series Based Structural Damage Detection Algorithm Using Gaussian Mixtures Modeling

[+] Author and Article Information
K. Krishnan Nair

John A. Blume Earthquake Engineering Center, Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305kknair@stanford.edu

Anne S. Kiremidjian1

Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305ask@stanford.edu

In the case when Ii is known, then this boils down to the case of fitting individual Gaussian distributions for each mixture.

1

Corresponding author.

J. Dyn. Sys., Meas., Control 129(3), 285-293 (Aug 23, 2006) (9 pages) doi:10.1115/1.2718241 History: Received November 18, 2005; Revised August 23, 2006

In this paper, a time series based detection algorithm is proposed utilizing the Gaussian Mixture Models. The two critical aspects of damage diagnosis that are investigated are detection and extent. The vibration signals obtained from the structure are modeled as autoregressive moving average (ARMA) processes. The feature vector used consists of the first three autoregressive coefficients obtained from the modeling of the vibration signals. Damage is detected by observing a migration of the extracted AR coefficients with damage. A Gaussian Mixture Model (GMM) is used to model the feature vector. Damage is detected using the gap statistic, which ascertains the optimal number of mixtures in a particular dataset. The Mahalanobis distance between the mixture in question and the baseline (undamaged) mixture is a good indicator of damage extent. Application cases from the ASCE Benchmark Structure simulated data have been used to test the efficacy of the algorithm. This approach provides a useful framework for data fusion, where different measurements such as strains, temperature, and humidity could be used for a more robust damage decision.

FIGURES IN THIS ARTICLE
<>
Copyright © 2007 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Migration of feature vectors with damage (엯, undamaged; *, damage pattern 1, ×, damage pattern 2)

Grahic Jump Location
Figure 2

Variation of log-likelihood function with number of mixtures

Grahic Jump Location
Figure 3

Illustration of within cluster distance

Grahic Jump Location
Figure 4

(a) Illustration of the ASCE Benchmark Structure (12); (b) sensor location and direction of acceleration signals in the ASCE Benchmark Structure (12)

Grahic Jump Location
Figure 5

Migration of the feature vectors with damage for minor patterns; (a) damage pattern 6 and (b) damage pattern 3 (undamaged, 엯; damaged, *; mixture center, +)

Grahic Jump Location
Figure 6

Migration of the feature vectors with damage for moderate patterns; (a) damage pattern 4 and (b) damage pattern 5 (undamaged 엯; damaged *; mixture center, +)

Grahic Jump Location
Figure 7

Migration of the feature vectors with damage for moderate patterns: (a) damage pattern 1 and (b) damage pattern 2 (undamaged, 엯; damaged, *; mixture center, +)

Grahic Jump Location
Figure 8

Variation of the distance metric DM with damage pattern

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In