Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15787
Title: Evaluation of fatigue cracks using nonlinearities of acousto-ultrasonic waves acquired by an active sensor network
Authors: Zhou, C
Hong, M
Su, Z 
Wang, Q
Cheng, L 
Issue Date: 2013
Publisher: Institute of Physics Publishing
Source: Smart materials and structures, 2013, v. 22, no. 1, 015018 How to cite?
Journal: Smart materials and structures 
Abstract: There has been increasing interest in using the nonlinear features of acousto-ultrasonic (AU) waves to detect damage onset (e.g., micro-fatigue cracks) due to their high sensitivity to damage with small dimensions. However, most existing approaches are able to infer the existence of fatigue damage qualitatively, but fail to further ascertain its location and severity. A damage characterization approach, in conjunction with the use of an active piezoelectric sensor network, was established, capable of evaluating fatigue cracks in a quantitative manner (including the co-presence of multiple fatigue cracks, and their individual locations and severities). Fundamental investigations, using both experiment and enhanced finite element analysis dedicated to the simulation of nonlinear AU waves, were carried out to link the accumulation of nonlinearities extracted from high-order AU waves to the characteristic parameters of a fatigue crack. A probability-based diagnostic imaging algorithm was developed, facilitating an intuitive presentation of identification results in images. The approach was verified experimentally by evaluating multi-fatigue cracks near rivet holes of a fatigued aluminum plate, showing satisfactory precision in characterizing real, barely visible fatigue cracks. Compared with existing methods, this approach innovatively (i) uses permanently integrated active sensor networks, conducive to automatic and online health monitoring; (ii) characterizes fatigue cracks at a quantitative level; (iii) allows detection of multiple fatigue cracks; and (iv) visualizes identification results in intuitive images.
URI: http://hdl.handle.net/10397/15787
ISSN: 0964-1726
EISSN: 1361-665X
DOI: 10.1088/0964-1726/22/1/015018
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