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TECHNICAL PAPERS

Using Ambient Vibrations to Detect Loosening of a Composite-to-Metal Bolted Joint in the Presence of Strong Temperature Fluctuations

[+] Author and Article Information
J. M. Nichols

 U.S. Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, DC 20375jonathan.nichols@nrl.navy.mil

S. T. Trickey, M. Seaver, S. R. Motley, E. D. Eisner

 U.S. Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, DC 20375

J. Vib. Acoust 129(6), 710-717 (Oct 03, 2006) (8 pages) doi:10.1115/1.2753502 History: Received July 28, 2006; Revised October 03, 2006

We present an approach for detecting damage-induced nonlinearities in structures. The method first involves the creation of surrogate data sets conforming to an appropriate null hypothesis (no damage). The second step is to then compare some nonlinear “feature” extracted from the original data to those extracted from the surrogates. Statistically significant differences suggest evidence in favor of the alternative hypothesis, damage. Using this approach we show how loose connections can be detected using ambient “wave” forcing, conforming to the Pierson-Moskowitz distribution, as the source of excitation. We also demonstrate the ability of this technique to operate without a recorded baseline data set and in the presence of widely varying temperatures. The structure in this case is a thick, composite beam bolted to a steel frame. Data are collected using an optical strain sensing system. For this experiment we are able to reliably detect the presence of a loosened bolt.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

Experimental setup

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Figure 2

Analytical Pierson-Moskowitz frequency distribution (left) and that recorded by the load cell (right)

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Figure 3

Original and sample surrogate time series for the data recorded at sensor 1 and sensor 2. Bottom row: Sample cross-spectrum and cross-correlation function associated with the original data and sample surrogates.

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Figure 4

Mean prediction error as a function of damage for varying temperature for surrogates (∙) and original data (+)

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Figure 5

Mean prediction error as a function of damage for varying temperature for surrogates (∙) and original data (+) for repeated experiment

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