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

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.

[1]  Kugiumtzis Surrogate data test for nonlinearity including nonmonotonic transforms , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[2]  Michael D. Todd,et al.  Use of data-driven phase space models in assessing the strength of a bolted connection in a composite beam , 2004 .

[3]  T. Schreiber,et al.  Surrogate time series , 1999, chao-dyn/9909037.

[4]  Hoon Sohn,et al.  A review of structural health monitoring literature 1996-2001 , 2002 .

[5]  A. Neiman,et al.  Surrogate analysis of coherent multichannel data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  A. G. Barnett,et al.  A time-domain test for some types of nonlinearity , 2005, IEEE Transactions on Signal Processing.

[7]  Louis M Pecora,et al.  A unified approach to attractor reconstruction. , 2007, Chaos.

[8]  Jonathan M. Nichols,et al.  A method for detecting damage-induced nonlinearities in structures using information theory , 2006 .

[9]  Theiler,et al.  Generating surrogate data for time series with several simultaneously measured variables. , 1994, Physical review letters.

[10]  Haym Benaroya,et al.  Nonlinear and Stochastic Dynamics of Compliant Offshore Structures , 2002 .

[11]  Michael D. Todd,et al.  A novel Bragg grating sensor interrogation system utilizing a scanning filter, a Mach-Zehnder interferometer and a 3×3 coupler , 2001 .

[12]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[13]  Schreiber,et al.  Improved Surrogate Data for Nonlinearity Tests. , 1996, Physical review letters.

[14]  Hoon Sohn,et al.  Combination of a Time Reversal Process and a Consecutiv Outlier Analysis for Baseline-free Damage Diagnosis , 2006 .

[15]  Jonathan M. Nichols,et al.  Detecting impact damage in experimental composite structures: an information-theoretic approach , 2006 .

[16]  K. Dolan,et al.  Surrogate for nonlinear time series analysis. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  J. Gentle,et al.  Randomization and Monte Carlo Methods in Biology. , 1990 .