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Data-driven methodology to detect and classify structural changes under temperature variations

Author
Anaya, M.; Tibuadiza, D.; Torres-Arredondo, M.A.; Pozo, F.; Ruiz, M.; Mujica, L.E.; Rodellar, J.; Fritzen, C.P
Type of activity
Journal article
Journal
Smart materials and structures
Date of publication
2014-04-01
Volume
23
Number
4
First page
045006-1
Last page
045006-21
DOI
https://doi.org/10.1088/0964-1726/23/4/045006 Open in new window
Project funding
Estructuras inteligentes: sistemas de monitorización e identificación de daños con aplicación en aeronáutica y en plantas eólicas
URL
http://iopscience.iop.org/article/10.1088/0964-1726/23/4/045006/meta;jsessionid=D58A555F66325B81CCA2B094C59BE09D.c6.iopscience.cld.iop.org Open in new window
Abstract
This paper presents a methodology for the detection and classification of structural changes under different temperature scenarios using a statistical data-driven modelling approach by means of a distributed piezoelectric active sensor network at different actuation phases. An initial baseline pattern for each actuation phase for the healthy structure is built by applying multiway principal component analysis (MPCA) to wavelet approximation coefficients calculated using the discrete wavelet tran...
Keywords
HEALTH, PERFORMANCE, PRINCIPAL COMPONENT ANALYSIS, SELF-ORGANIZING MAP, VALIDATION, WAVES, damage classification, damage index, discrete wavelet transform (DWT), principal component analysis (PCA), self-organizing maps (SOM), structural health monitoring (SHM), temperature effects
Group of research
CoDAlab - Control, Dynamics and Applications

Participants