Vibration-based damage identification in beam-like composite laminates by using artificial neural networks

Sahin M., Shenoi R.

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, cilt.217, sa.6, ss.661-676, 2003 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 217 Sayı: 6
  • Basım Tarihi: 2003
  • Doi Numarası: 10.1243/095440603321919581
  • Dergi Adı: PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.661-676
  • Anahtar Kelimeler: vibration-based analysis, curvature mode shape, artificial neural networks, damage quantification, damage localization, composite structures, finite element modelling, noise, STRUCTURAL DAMAGE, IMAGE SEQUENCES, FINITE-ELEMENT, EXTRACTION, WAVELETS
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

This paper investigates the effectiveness of the combination of global (changes in natural frequencies) and local (curvature mode shapes) vibration-based analysis data as input for artificial neural networks (ANNs) for location and severity prediction of damage in fibre-reinforced plastic laminates. A finite element analysis tool has been used to obtain the dynamic characteristics of intact and damaged cantilever composite beams for the first three natural modes. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the finite element model of the beam structure. After performing the sensitivity analyses aimed at finding the necessary parameters for the damage detection, different input-output sets have been introduced to various ANNs. In order to check the robustness of the input used in the analysis, random noise has been generated numerically and added to noise-free data during the training of the ANNs. Finally, trained feedforward back-propagation ANNs have been tested using new damage cases and checks have been made for severity and location prediction of the damage.