H. Sohn, Effects of environmental and operational variability on structural health monitoring, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.31, issue.168, pp.365-539, 1851.
DOI : 10.1098/rsta.2006.1935

C. Fritzen, G. Mengelkamp, and A. Guemes, Elimination of temperature effects on damage detection within a smart structure concept, Proceedings of the 4th International Workshop on Structural Health Monitoring, pp.1530-1538, 2003.

J. Moll, P. Kraemer, and C. Fritzen, Compensation of Environmental Influences for Damage Detection using Classification Techniques, Proc. 4th European Workshop on Structural Health Monitoring, 2008.

I. Buethe, P. Kraemer, and C. Fritzen, Applications of Self-Organizing Maps in Structural Health Monitoring, Key Engineering Materials, vol.518, issue.518, pp.37-46, 2012.
DOI : 10.4028/www.scientific.net/KEM.518.37

T. Arredondo, M. A. , and C. Fritzen, Ultrasonic guided wave dispersive characteristics in composite structures under variable temperature and operational conditions, Proceedings of the 6th European Workshop in Structural Health Monitoring, EWSHM 2012, pp.2012-261

T. Arredondo, M. A. , D. Tibaduiza, L. E. Mujica, J. Rodellar et al., Data-Driven Multivariate Algorithms for Damage Detection and Identification: Evaluation and Comparison, International Journal of Structural Health Monitoring, issue.DOI, pp.2013-2023

T. Arredondo and M. A. , Acoustic Emission Testing and Acousto-Ultrasonics for Structural Health Monitoring, 2013.

Y. Qiu and S. Backer, Modeling for Suppression of Moisture/Temperature Induced Dimensional Changes in Fibrous Composite Structures, Journal of the Textile Institute, vol.58, issue.2, pp.257-270, 2008.
DOI : 10.1177/002199837601000203

S. G. Mallat, A Theory for Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, issue.117, pp.674-693, 1989.
DOI : 10.1515/9781400827268.494

D. Tibaduiza, M. Arredondo, L. E. Mujica, J. Rodellar, and C. Fritzen, A study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring, Mechanical Systems and Signal Processing, pp.467-484, 2013.
DOI : 10.1016/j.ymssp.2013.05.020

T. Arredondo, M. , D. Tibaduiza, M. Mcgugan, H. Toftegaard et al., Multivariate data-driven modelling and pattern recognition for damage detection and identification for acoustic emission and acousto-ultrasonics, Journal of Smart Materials and Structures, issue.10, pp.22-23, 2013.

G. Cabanes and Y. Bennani, Learning the Number of Clusters in Self Organizing Map, Self- Organizing Maps, 2010.
DOI : 10.5772/9164

G. Cabanes, Y. Bennani, and D. Fresneau, Enriched topological learning for cluster detection and visualization, Neural Networks, vol.32, pp.186-195, 2012.
DOI : 10.1016/j.neunet.2012.02.019
URL : https://hal.archives-ouvertes.fr/hal-01461451