DocumentCode :
2373679
Title :
Three-way analysis of Structural Health Monitoring data
Author :
Prada, Miguel A. ; Hollmé, Jaakko ; Toivola, Janne ; Kullaa, Jyrki
Author_Institution :
Sch. of Sci. & Technol., Dept. of Inf. & Comput. Sci., Aalto Univ., Aalto, Finland
fYear :
2010
fDate :
Aug. 29 2010-Sept. 1 2010
Firstpage :
256
Lastpage :
261
Abstract :
Structural Health Monitoring aims to identify damages in engineering structures by monitoring changes in their vibration response. Unsupervised learning algorithms can be used to obtain a model of the undamaged condition and detect which samples are not in agreement with it. However, in real structures with a sensor network configuration, the number of candidate features usually becomes large. Therefore, complexity increases and it is necessary to perform feature selection and/or dimensionality reduction. We propose to exploit the three-way structure of data and apply a true multi-way data analysis algorithm: parallel factor analysis. A simple model is obtained and used to train accurate novelty detectors. The methods are tested both with real and simulated structural data to assess that three-way analysis can be successfully used in structural health monitoring.
Keywords :
condition monitoring; data analysis; feature extraction; structural engineering computing; unsupervised learning; data analysis algorithm; engineering structure; feature selection; sensor network; structural health monitoring data; three way analysis; unsupervised learning algorithm; vibration response; Brain modeling; Computational modeling; Feature extraction; Load modeling; Monitoring; Time frequency analysis; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop on
Conference_Location :
Kittila
ISSN :
1551-2541
Print_ISBN :
978-1-4244-7875-0
Electronic_ISBN :
1551-2541
Type :
conf
DOI :
10.1109/MLSP.2010.5589252
Filename :
5589252
Link To Document :
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