Impedance based damage detection under varying temperature and loading conditions

Hyung Jin Lim, Minkoo Kim, Hoon Sohn, Chan Yik Park

Research output: Journal article publicationJournal articleAcademic researchpeer-review

91 Citations (Scopus)


The impedance based damage detection technique utilizing piezoelectric materials has become a promising and attractive tool for structural health monitoring due to its high sensitivity to small local damage. However, impedance signals are also sensitive to time-varying environmental and operational conditions, and these ambient variations can often cause false-alarms. In this study, a data normalization technique using Kernel principal component analysis (KPCA) is developed to improve damage detectability under varying temperature and external loading conditions and to minimize false-alarms due to these variations. The proposed technique is used to detect bolt loosening within a metal fitting lug, which connects a composite aircraft wing to a fuselage. Model and full-scale tests are performed under realistic temperature and loading variations to validate the proposed technique. The uniqueness of this paper lies in that (1) a data normalization technique tailored for impedance based damage detection has been developed (2) multiple environmental parameters, such as temperature and static/dynamic loading are considered simultaneously for data normalization and (3) the effectiveness of the proposed technique is examined using data collected from a full-scale composite wing specimen with a complex geometry.
Original languageEnglish
Pages (from-to)740-750
Number of pages11
JournalNDT and E International
Issue number8
Publication statusPublished - 1 Dec 2011
Externally publishedYes


  • Data normalization
  • Environmental and operational variation
  • Impedance based damage detection
  • Kernel principal component analysis
  • Pattern recognition

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanical Engineering


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