title: A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications.
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.
authors.affiliation: 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. jaimevitola@usantotomas.edu.co.|MEM (Materials-Electronics and Modelling Research Group), Faculty of Electronics Engineering, Universidad Santo Tomás, Cra 9 No. 51-11, Bogotá 110231, Colombia. jaimevitola@usantotomas.edu.co.
authors.forename: Jaime
authors.initials: J
authors.lastname: Vitola
authors.affiliation: 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. francesc.pozo@upc.edu.
authors.forename: Francesc
authors.initials: F
authors.lastname: Pozo
authors.affiliation: Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia. dtibaduizab@unal.edu.co.
authors.forename: Diego A
authors.initials: DA
authors.lastname: Tibaduiza
authors.affiliation: Faculty of Engineering, Fundación Universitaria Los Libertadores, Cra 16 No. 63A-68, Bogotá 111221, Colombia. maribel.anaya@libertadores.edu.co.
authors.forename: Maribel
authors.initials: M
authors.lastname: Anaya
keywords: active system; damage classification; data fusion; machine learning; piezoelectric; sensors