Probability-based diagnostic imaging using hybrid features extracted from ultrasonic Lamb wave signals

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81 Citations (Scopus)

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.
Original languageEnglish
Article number125005
JournalSmart Materials and Structures
Volume20
Issue number12
DOIs
Publication statusPublished - 1 Dec 2011

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics
  • Civil and Structural Engineering
  • Condensed Matter Physics
  • Mechanics of Materials
  • Materials Science(all)

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