Paper Info
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.
Year
DOI
Venue
2017
10.3390/s17020417
SENSORS
Keywords
Field
DocType
piezoelectric,sensors,active system,data fusion,machine learning,damage classification
k-nearest neighbors algorithm,Signal processing,Data mining,Feature vector,Structural health monitoring,Automation,Sensor fusion,Piezoelectric sensor,Engineering,Active systems
Journal
Volume
Issue
ISSN
17
2.0
1424-8220
Citations 
PageRank 
References 
10
0.74
4
Authors
4
Name
Order
Citations
PageRank
Jaime Vitola1141.66
Francesc Pozo2208.13
Diego Alexander Tibaduiza Burgos3141.66
Maribel Anaya Vejar4100.74