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Sensors 2018, 18(5), 1571; https://doi.org/10.3390/s18051571

Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

1
Department of Mathematics, Escola d’Enginyeria de Barcelona Est. (EEBE), Universitat Politécnica de Catalunya (UPC) Barcelonatech, Campus Diagonal Besòs, Edifici A, C. Eduard Maristany, 10-14, 08019 Barcelona, Spain
2
CEMOS Research Group, Electrical, Electronics and Telecommunications Engineering School, Universidad Industrial de Santander (UIS), Bucaramanga 680002, Colombia
Current address: Campus Diagonal Besòs, Edifici A, C. Eduard Maristany, 6-12. 08930 St Adrià de Besòs, Barcelona, Spain.
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 10 January 2018 / Revised: 23 March 2018 / Accepted: 23 March 2018 / Published: 15 May 2018
(This article belongs to the Special Issue Sensor Technologies for Health Monitoring of Composite Structures)

Abstract

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. View Full-Text
Keywords: piezodiagnostics; Baseline Models; Damage Statistical Analysis; principal component analysis; structural damage detection piezodiagnostics; Baseline Models; Damage Statistical Analysis; principal component analysis; structural damage detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style

Camacho Navarro, J.; Ruiz, M.; Villamizar, R.; Mujica, L.; Quiroga, J. Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment. Sensors 2018, 18, 1571.

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