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

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 red...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2017-02, Vol.17 (2), p.417-417
Main Authors: Vitola, Jaime, Pozo, Francesc, Tibaduiza, Diego A, Anaya, Maribel
Format: Article
Language:English
Subjects:
active system
Automation
Classification
damage classification
Damage detection
data fusion
Data integration
Detectors
Dynamic response
Dynamical systems
Imatges
Inspection
machine learning
Matemàtiques i estadística
Military applications
Military engineering
Multisensor fusion
Pattern analysis
Pattern classification
Pattern perception
Pattern recognition
Piezoelectric
Piezoelectricity
Processament
Reconeixement de formes (Informàtica)
Sensors
Signal processing
Structural damage
Structural health monitoring
Àrees temàtiques de la UPC
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s17020417