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Vibration-based structural damage identification using wavelet transform

Författare

Summary, in English

This paper presents a new damage identification technique based on the statistical moments of the energy density function of the vibration responses in the time-scale (or time-frequency) domain. The continuous wavelet transform is first conducted to decompose the vibration responses into discrete energy distributions as a joint function of time and scale. The principal structural response features are then extracted from the energy density functions using moments. Consequently, the zeroth-order moment (ZOM) known as the total energy of the joint density function is computed at each measurement grid point for the pre-damage and post-damage states and is then implemented for detection and localization of damage in a concrete plate model and in a steel plate girder of a bridge structure. The significant contribution is that the wavelet coefficients are transformed into a new damage identification parameter in the space domain which is considered to be a novel application of the wavelet analysis coefficients. The major advantage is that the time-frequency analysis conducted using the wavelet transform provides a powerful tool to characterize deterministic as well as random (stationary and non-stationary) responses and can be used to detect slight changes in the response characteristics and local variations. Finally, comparison of the results obtained from the proposed method and those obtained from existing non-model-based damage identification techniques shows that the proposed method is more sensitive to damage than these other methods.

Publiceringsår

2008

Språk

Engelska

Sidor

1194-1215

Publikation/Tidskrift/Serie

Mechanical Systems and Signal Processing

Volym

22

Issue

5

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Building Technologies

Nyckelord

  • Statistical moments
  • Damage localization
  • Wavelet transform
  • Plate
  • Bridge

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1096-1216