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Sensors 2017, 17(2), 417; doi:10.3390/s17020417

A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications

1,4,†
,
1,†,* , 2,†
and
3,†
1
Control, Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 6–12, Sant Adrià de Besòs, Barcelona 08930, Spain
2
Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia
3
Faculty of Engineering, Fundación Universitaria Los Libertadores, Cra 16 No. 63A-68, Bogotá 111221, Colombia
4
MEM (Materials-Electronics and Modelling Research Group), Faculty of Electronics Engineering, Universidad Santo Tomás, Cra 9 No. 51-11, Bogotá 110231, Colombia
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Stefan Bosse, Ansgar Trächtler, Klaus-Dieter Thoben, Berend Denkena and Dirk Lehmhus
Received: 30 December 2016 / Revised: 9 February 2017 / Accepted: 17 February 2017 / Published: 21 February 2017
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems)

Abstract

Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed. View Full-Text
Keywords: piezoelectric; sensors; active system; data fusion; machine learning; damage classification piezoelectric; sensors; active system; data fusion; machine learning; damage classification
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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

Vitola, J.; Pozo, F.; Tibaduiza, D.A.; Anaya, M. A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications. Sensors 2017, 17, 417.

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