Features for damage detection with insensitivity to environmental and operational variations

Elizabeth Cross, Graham Manson, Keith Worden, Stephen Pierce

Research output: Contribution to journalArticle

65 Citations (Scopus)

Abstract

This paper explores and compares the application of three different approaches to the data normalization problem in structural health monitoring (SHM), which concerns the removal of confounding trends induced by varying operational conditions from a measured structural response that correlates with damage. The methodologies for singling out or creating damage-sensitive features that are insensitive to environmental influences explored here include cointegration, outlier analysis and an approach relying on principal component analysis. The application of cointegration is a new idea for SHM from the field of econometrics, and this is the first work in which it has been comprehensively applied to an SHM problem. Results when applying cointegration are compared with results from the more familiar outlier analysis and an approach that uses minor principal components. The ability of these methods for removing the effects of environmental/operational variations from damage-sensitive features is demonstrated and compared with benchmark data from the Brite-Euram project DAMASCOS (BE97 4213), which was collected from a Lamb-wave inspection of a composite panel subject to temperature variations in an environmental chamber.

LanguageEnglish
Number of pages26
JournalProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Early online date10 Oct 2012
DOIs
Publication statusE-pub ahead of print - 10 Oct 2012

Fingerprint

Damage Detection
Insensitivity
Cointegration
structural health monitoring
Damage detection
Structural health monitoring
Health Monitoring
Damage
damage
Outlier
sensitivity
econometrics
Environmental chambers
Lamb Waves
test chambers
Lamb waves
Confounding
Principal Components
principal components analysis
Econometrics

Keywords

  • damage detection
  • insensitivity
  • environmental variations
  • operational variations

Cite this

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