A sensor data fusion system based on k-Nearest neighbor pattern classification for structural health monitoring applications
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2017-02-21
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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.
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Atribución-NoComercial-CompartirIgual 2.5 Colombia