Features for damage detection with insensitivity to environmental and operational variations

By Elizabeth J. Cross, G. Manson, Keith Worden, Stephen Pierce. Proceedings of the Royal Society A, 2012

155 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2d705837110437630e93be30f7381ae3ad1cca3f

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Citations in the corpus

A review of damage detection methods for wind turbine blades (2015, methodology): “…and operational influences of turbine blades, data normalization methods such as factor analysis, statistical means (Sohn 2007), cointegration, outlier analysis and principal component analysis (Cross et al 2012) and recent state- space reconstruction (Figueiredo et al 2010) maybe employed.”
A comparison of linear approaches to filter out environmental effects in structural health monitoring (2018, methodology): “The way principal component analysis can be used to filter out confounding effects is to project the feature vector onto the subspace of the minor components, when the major components have been identified on data containing variability to confounding effects [28,29].”