Paper
18 August 2003 Singularity detection for structural health monitoring using Holder exponents
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Abstract
The majority of structural health monitoring studies reported in the technical literature focus on identifying damage sensitive features that can be extracted from dynamic response data. However, many of these studies assume the structure can be modeled as a linear system before and after damage and use parameters of these models as the damage sensitive features. The study summarized in this paper proposes a damage sensitive feature that takes advantage of the nonlinearities associated with discontinuities introduced into the dynamic response data as a result of certain types of damage. Specifically, the Holder exponent, a measure of the degree to which a signal is differentiable, is the feature that is used to detect the presence of damage and when that damage occurred. A procedure for capturing the time varying nature of the Holder exponent based on wavelet transforms is demonstrated through applications to non-stationary random signals with underlying discontinuities and then to a harmonically excited mechanical system that contains a loose part. Also, a classification procedure is developed to quantify when changes in the Holder exponent are significant. The results presented herein show the Holder exponent to be an effective feature for identifying damage that introduces discontinuities into the measured dynamic response data.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Amy N. Robertson, Charles R. Farrar, and Hoon Sohn "Singularity detection for structural health monitoring using Holder exponents", Proc. SPIE 5057, Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures, (18 August 2003); https://doi.org/10.1117/12.482762
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Cited by 2 scholarly publications.
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KEYWORDS
Wavelets

Wavelet transforms

Earthquakes

Structural health monitoring

Signal detection

Fourier transforms

Detection and tracking algorithms

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