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Title: Uncertainty quantification for acoustic nonlinearity parameter in lamb wave-based prediction of barely visible impact damage in composites
Authors: Hong, M 
Mao, Z
Todd, MD
Su, ZQ 
Issue Date: Jan-2017
Source: Mechanical systems and signal processing, 1 Jan. 2017, v. 82, p. 448-460
Abstract: Nonlinear features extracted from Lamb wave signals (e.g., second harmonic generation) are demonstrably sensitive to microscopic damage, such as fatigue and material thermal degradation. While a majority of the existing studies in this context is focused on detecting undersized damage in metallic materials, the present study is aimed at expanding such a detection philosophy to the domain of composites, by linking the relative acoustic nonlinearity parameter (RANP) - a prominent nonlinear signal feature of Lamb waves - to barely visible impact damage (BVID) in composites. Nevertheless, considering immense uncertainties inevitably embedded in acquired signals (due to instrumentation, environment, operation, computation/estimation, etc.) which can adversely obfuscate nonlinear features, it is necessary to quantify the uncertainty of the RANP (i.e., its statistics) in order to enhance decision-making associated with its use as a detection feature. A probabilistic model is established to numerically evaluate the statistical distribution of the RANP. Using piezoelectric wafers, Lamb waves are acquired and processed to produce histograms of RANP estimates in both the healthy and damaged conditions of a CF/EP laminate, to which the model is compared, with good agreement observed between the model-predicted and experimentally-obtained statistic distributions of the RANP. With the model, BVID in the laminate is predicted. The model is further made use of to quantify the level of confidence in damage prediction results based on the concept of a receiver operating characteristic, enabling the practitioners to better understand the obtained results in the presence of uncertainties.
Keywords: Statistical signal processing
Uncertainty quantification
Probabilistic modeling
Relative acoustic nonlinearity parameter
Lamb waves
Damage identification
Publisher: Academic Press
Journal: Mechanical systems and signal processing 
ISSN: 0888-3270
EISSN: 1096-1216
DOI: 10.1016/j.ymssp.2016.05.035
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