Abstract:
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This work discusses the advantage of using cross-correlation analysis in a data-driven
approach based on principal component analysis (PCA) and piezodiagnostics to obtain successful
diagnosis of events in structural health monitoring (SHM). In this sense, the identification of noisy
data and outliers, as well as the management of data cleansing stages can be facilitated through the
implementation of a preprocessing stage based on cross-correlation functions. Additionally, this
work evidences an improvement in damage detection when the cross-correlation is included as part
of the whole damage assessment approach. The proposed methodology is validated by processing
data measurements from piezoelectric devices (PZT), which are used in a piezodiagnostics approach
based on PCA and baseline modeling. Thus, the influence of cross-correlation analysis used in the
preprocessing stage is evaluated for damage detection by means of statistical plots and self-organizing
maps. Three laboratory specimens were used as test structures in order to demonstrate the validity of
the methodology: (i) a carbon steel pipe section with leak and mass damage types, (ii) an aircraft
wing specimen, and (iii) a blade of a commercial aircraft turbine, where damages are specified as
mass-added. As the main concluding remark, the suitability of cross-correlation features combined
with a PCA-based piezodiagnostic approach in order to achieve a more robust damage assessment
algorithm is verified for SHM tasks. |