Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21264
Title: Probability-based diagnostic imaging using hybrid features extracted from ultrasonic Lamb wave signals
Authors: Zhou, C
Su, Z 
Cheng, L 
Issue Date: 2011
Publisher: Institute of Physics Publishing
Source: Smart materials and structures, 2011, v. 20, no. 12, 125005 How to cite?
Journal: Smart materials and structures 
Abstract: The imaging technique based on guided waves has been a research focus in the field of damage detection over the years, aimed at intuitively highlighting structural damage in two-or three-dimensional images. The accuracy and efficiency of this technique substantially rely on the means of defining the field values at image pixels. In this study, a novel probability-based diagnostic imaging (PDI) approach was developed. Hybrid signal features (including temporal information, intensity of signal energy and signal correlation) were extracted from ultrasonic Lamb wave signals and integrated to retrofit the traditional way of defining field values. To acquire hybrid signal features, an active sensor network in line with pulse-echo and pitch-catch configurations was designed, supplemented with a novel concept of 'virtual sensing'. A hybrid image fusion scheme was developed to enhance the tolerance of the approach to measurement noise/uncertainties and erroneous perceptions from individual sensors. As applications, the approach was employed to identify representative damage scenarios including L-shape through-thickness crack (orientation-specific damage), polygonal damage (multi-edge damage) and multi-damage in structural plates. Results have corroborated that the developed PDI approach based on the use of hybrid signal features is capable of visualizing structural damage quantitatively, regardless of damage shape and number, by highlighting its individual edges in an easily interpretable binary image.
URI: http://hdl.handle.net/10397/21264
ISSN: 0964-1726
EISSN: 1361-665X
DOI: 10.1088/0964-1726/20/12/125005
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