Next Article in Journal
Nanoporous Gold Films Prepared by a Combination of Sputtering and Dealloying for Trace Detection of Benzo[a]pyrene Based on Surface Plasmon Resonance Spectroscopy
Previous Article in Journal
Metallic Glass/PVDF Magnetoelectric Laminates for Resonant Sensors and Actuators: A Review
Open AccessArticle

Distributed Piezoelectric Sensor System for Damage Identification in Structures Subjected to Temperature Changes

by Jaime Vitola 1,2,†, Francesc Pozo 1,*,†, Diego A. Tibaduiza 3,† and Maribel Anaya 4,†
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
MEM (Modelling-Electronics and Monitoring Research Group), Faculty of Electronics Engineering, Universidad Santo Tomás, Bogotá 110231, Colombia
3
Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia
4
Facultad de Ingeniería, Fundación Universitaria Los Libertadores, Carrera 16 No. 63A-68, Bogotá 111221, Colombia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Vittorio M. N. Passaro
Sensors 2017, 17(6), 1252; https://doi.org/10.3390/s17061252
Received: 24 April 2017 / Revised: 25 May 2017 / Accepted: 26 May 2017 / Published: 31 May 2017
(This article belongs to the Section Physical Sensors)
Structural health monitoring (SHM) is a very important area in a wide spectrum of fields and engineering applications. With an SHM system, it is possible to reduce the number of non-necessary inspection tasks, the associated risk and the maintenance cost in a wide range of structures during their lifetime. One of the problems in the detection and classification of damage are the constant changes in the operational and environmental conditions. Small changes of these conditions can be considered by the SHM system as damage even though the structure is healthy. Several applications for monitoring of structures have been developed and reported in the literature, and some of them include temperature compensation techniques. In real applications, however, digital processing technologies have proven their value by: (i) offering a very interesting way to acquire information from the structures under test; (ii) applying methodologies to provide a robust analysis; and (iii) performing a damage identification with a practical useful accuracy. This work shows the implementation of an SHM system based on the use of piezoelectric (PZT) sensors for inspecting a structure subjected to temperature changes. The methodology includes the use of multivariate analysis, sensor data fusion and machine learning approaches. The methodology is tested and evaluated with aluminum and composite structures that are subjected to temperature variations. Results show that damage can be detected and classified in all of the cases in spite of the temperature changes. View Full-Text
Keywords: machine learning; principal component analysis; piezoelectric sensors; temperature variations, damage classification machine learning; principal component analysis; piezoelectric sensors; temperature variations, damage classification
Show Figures

Figure 1

MDPI and ACS Style

Vitola, J.; Pozo, F.; Tibaduiza, D.A.; Anaya, M. Distributed Piezoelectric Sensor System for Damage Identification in Structures Subjected to Temperature Changes. Sensors 2017, 17, 1252.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop