Quantitative evaluation of orientation-specific damage using elastic waves and probability-based diagnostic imaging

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80 Citations (Scopus)


Different from the damage with relatively smooth boundaries or edges such as a through-thickness hole or delamination which scatters elastic waves omnidirectionally, orientation-specific damage of sizable length in a particular dimension (e.g., a crack or a notch) often exerts strong directivity to elastic wave propagation. As a consequence the damage-scattered waves may not be captured efficiently by sensors at certain locations, posing a challenging issue to elastic-wave-based damage identification. In this study, the influence of damage orientation on Lamb wave propagation was quantitatively scrutinised. Based on the established correlation between damage parameters (location, orientation, shape and size) and extracted signal features, a probability-based diagnostic imaging approach was developed, in conjunction with use of an active sensor network in conformity to a pulseecho configuration. Relying on enhancive signal features including both the temporal information and signal intensity, this imaging approach is capable of indicating the orientation of individual damage edges clearly and further shape/size of the damage. The effectiveness of the approach was demonstrated by predicting orientation-specific damage cases including a triangular through-thickness hole (through finite element simulation), a through-thickness crack and an L-shape crack (through experiment) in aluminium plates. With the assistance of a two-level synthetic image fusion scheme, all damage cases were visually and quantitatively highlighted in the probability images.
Original languageEnglish
Pages (from-to)2135-2156
Number of pages22
JournalMechanical Systems and Signal Processing
Issue number6
Publication statusPublished - 1 Aug 2011


  • Damage identification
  • Lamb waves
  • Orientation-specific damage
  • Probability-based diagnostic imaging
  • Signal processing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications


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