H. D. Abarbanel, R. Brown, J. J. Sidorowich, and L. S. Tsimring, The analysis of observed chaotic data in physical systems, Reviews of Modern Physics, vol.65, issue.4, pp.1331-1392, 1993.
DOI : 10.1103/RevModPhys.65.1331

A. Alvandi, Contribution à l'utilisation pratique de l'évaluaton dynamique pour la détection d'endommagements dans les ponts, Ecole Nationale Des Ponts Et Chaussées, vol.20, p.10, 2003.

A. Alvandi, J. Bastien, E. Gregoire, and E. M. Jolin, BRIDGE INTEGRITY ASSESSMENT BY CONTINUOUS WAVELET TRANSFORMS, International Journal of Structural Stability and Dynamics, vol.09, issue.01, pp.11-43, 2009.
DOI : 10.1142/S0219455409002874

M. Basseville, A. Benveniste, M. Goursat, L. Hermans, and L. Mevel, Output-Only Subspace-Based Structural Identification: From Theory to Industrial Testing Practice, Journal of Dynamic Systems, Measurement, and Control, vol.123, issue.4, pp.668-676, 2001.
DOI : 10.1115/1.1410919

L. Bornn, C. R. Farrar, and E. G. Park, Damage detection in initially nonlinear systems, International Journal of Engineering Science, vol.48, issue.10, pp.909-920, 2010.
DOI : 10.1016/j.ijengsci.2010.05.011

L. Bornn, C. R. Farrar, G. Park, and K. Farinholt, Structural Health Monitoring With Autoregressive Support Vector Machines, Journal of Vibration and Acoustics, vol.131, issue.2, p.21004, 2009.
DOI : 10.1115/1.3025827

G. E. Box and G. M. Jenkins, Time series analysis : forecasting and control, 1994.
DOI : 10.1002/9781118619193

P. J. Brockwell and R. A. Davis, Time Series : Theory and Methods, 1991.

R. Brown, P. Bryant, and H. D. , Computing the Lyapunov spectrum of a dynamical system from an observed time series, Physical Review A, vol.43, issue.6, pp.2787-2834, 1991.
DOI : 10.1103/PhysRevA.43.2787

C. J. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.121-167, 1998.
DOI : 10.1023/A:1009715923555

E. P. Carden and P. Fanning, Vibration Based Condition Monitoring: A Review, Structural Health Monitoring, vol.3, issue.4, p.355, 2004.
DOI : 10.1177/1475921704047500

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.3093

F. Casciati and S. Casciati, Structural health monitoring by lyapunov exponents of non-linear time series. Structural Control and Health Monitoring, pp.132-146, 2006.

E. Castillo, A. S. Hadi, J. M. Alegría, and E. N. Balakrishnan, Extreme value and related models with applications in engineering and science, 2005.

F. , N. Catbas, and A. Emin-aktan, Condition and damage assessment : Issues and some promising indices, Journal of Structural Engineering, vol.128, issue.8, pp.1026-1036, 2002.
DOI : 10.1061/(asce)0733-9445(2002)128:8(1026)

V. Chandola, A. Banerjee, and E. V. Kumar, Anomaly detection, ACM Computing Surveys, vol.41, issue.3, p.15, 2009.
DOI : 10.1145/1541880.1541882

V. Cherkassky and Y. Ma, Practical selection of SVM parameters and noise estimation for SVM regression, Neural Networks, vol.17, issue.1, pp.113-126, 2004.
DOI : 10.1016/S0893-6080(03)00169-2

A. Cheung, C. Cabrera, P. Sarabandi, A. Nair, E. H. Kiremidjian et al., The application of statistical pattern recognition methods for damage detection to field data, Smart Materials and Structures, vol.17, issue.6, 2008.
DOI : 10.1088/0964-1726/17/6/065023

C. Craig, R. D. Neilson, and E. J. Penman, THE USE OF CORRELATION DIMENSION IN CONDITION MONITORING OF SYSTEMS WITH CLEARANCE, Journal of Sound and Vibration, vol.231, issue.1, pp.1-17, 2000.
DOI : 10.1006/jsvi.1998.2713

C. Crémona, Surveillance de santé structurale, la démarche du projet s3, Proceedings of GC'2009, 2009.

A. Cury and C. Crémona, Pattern recognition of structural behaviors based on learning algorithms and symbolic data concepts. Structural Control and Health Monitoring, pp.15-27, 2010.

A. Cury, C. Crémona, and E. E. Diday, Application of symbolic data analysis for structural modification assessment, Engineering Structures, vol.32, issue.3, pp.762-775, 2010.
DOI : 10.1016/j.engstruct.2009.12.004

G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals, and Systems (MCSS), pp.303-314, 1989.

S. Das, A. N. Srivastava, and E. A. Chattopadhyay, Classification of Damage Signatures in Composite Plates using One-Class SVMs, 2007 IEEE Aerospace Conference, pp.1-19, 2007.
DOI : 10.1109/AERO.2007.352912

S. W. Doebling, C. R. Farrar, and M. B. Prime, A summary review of vibrationbased damage identification methods, Los Alamos National Laboratory, pp.1-34, 1997.

S. W. Doebling, C. R. Farrar, M. B. Prime, and D. W. Shevitz, Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics : A literature review, Los Alamos National Lab., NM (United States), p.10, 1920.
DOI : 10.2172/249299

J. P. Eckmann, S. O. Kamphorst, D. Ruelle, and E. S. Ciliberto, Liapunov exponents from time series, Physical Review A, vol.34, issue.6, pp.4971-4979, 1986.
DOI : 10.1103/PhysRevA.34.4971

C. R. Farrar, . Baker, . Bell, . Cone, . Darling et al., Dynamic characterization and damage detection in the i-40 bridge over the rio grande. Rapport technique, Los Alamos National Lab, NM (United States), 1994.

C. R. Farrar, P. J. Cornwell, S. W. Doebling, and M. B. Prime, Structural health monitoring studies of the alamosa canyon and i-40 bridges, pp.11-12, 2000.

C. R. Farrar and S. W. Doebling, Lessons learned from applications of vibration-based damage identification methods to a large bridge structure, 1997.

C. R. Farrar and K. Worden, An introduction to structural health monitoring, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.365, issue.1851, p.303, 1851.
DOI : 10.1098/rsta.2006.1928

C. R. Farrar, K. Worden, M. D. Todd, G. Park, D. E. Nichols et al., Nonlinear system identification for damage detection, Report, 2007.
DOI : 10.2172/922532

E. Figueiredo, G. Park, J. Figueiras, C. Farrar, and E. K. Worden, Structural health monitoring algorithm comparisons using standard data sets. Rapport technique, Los Alamos National Laboratory, p.17, 2009.
DOI : 10.2172/961604

URL : http://www.osti.gov/scitech/servlets/purl/961604

E. Figueiredo, M. D. Todd, C. R. Farrar, and E. E. Flynn, Autoregressive modeling with state-space embedding vectors for damage detection under operational variability, International Journal of Engineering Science, vol.48, issue.10, pp.2010-62
DOI : 10.1016/j.ijengsci.2010.05.005

A. M. Fraser and H. L. Swinney, Independent coordinates for strange attractors from mutual information, Physical Review A, vol.33, issue.2, pp.1134-1140, 1986.
DOI : 10.1103/PhysRevA.33.1134

M. L. Fugate, H. Sohn, and C. R. Farrar, Unsupervised learning methods for vibration-based damage detection, Proceedings of 18th International Modal Analysis Conference?IMAC. Citeseer, pp.18-23, 2000.

G. V. Garcia and R. Osegueda, Combining damage index method and arma method to improve damage detection, IMAC-XVIII : A Conference on Structural Dynamics, pp.668-673, 2000.

K. Geist, U. Parlitz, and E. W. Lauterborn, Comparison of different methods for computing lyapunov exponents. Progress of Theoretical Physics, pp.875-893, 1990.

S. Ghafari, F. Golnaraghi, and E. F. Ismail, Effect of localized faults on chaotic vibration of rolling element bearings, Nonlinear Dynamics, vol.60, issue.4, pp.287-301, 2008.
DOI : 10.1007/s11071-007-9314-2

D. F. Giraldo, S. J. Dyke, and J. M. Caicedo, Damage Detection Accommodating Varying Environmental Conditions, Structural Health Monitoring, vol.5, issue.2, pp.155-167, 2006.
DOI : 10.1177/1475921706057987

S. Girard, Contribution à l'inférence statistique semi-et non-paramétrique

P. Grassberger and I. Procaccia, Characterization of Strange Attractors, Physical Review Letters, vol.50, issue.5, pp.346-349, 1983.
DOI : 10.1103/PhysRevLett.50.346

M. Gul and F. N. Catbas, Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications, Mechanical Systems and Signal Processing, pp.2192-2204, 2009.
DOI : 10.1016/j.ymssp.2009.02.013

N. Haritos and J. S. Owen, The Use of Vibration Data for Damage Detection in Bridges: A Comparison of System Identification and Pattern Recognition Approaches, Structural Health Monitoring, vol.3, issue.2, p.141, 2004.
DOI : 10.1177/1475921704042698

L. M. Hively, P. C. Gailey, and V. A. Protopopescu, Detecting dynamical change in nonlinear time series, Physics Letters A, vol.258, issue.2-3, pp.103-114, 1999.
DOI : 10.1016/S0375-9601(99)00342-4

V. Hodge and J. Austin, A Survey of Outlier Detection Methodologies, Artificial Intelligence Review, vol.22, issue.2, pp.85-126, 2004.
DOI : 10.1023/B:AIRE.0000045502.10941.a9

J. R. Hosking and J. R. Wallis, Parameter and Quantile Estimation for the Generalized Pareto Distribution, Technometrics, vol.4, issue.3, pp.339-349, 1987.
DOI : 10.1016/0022-1694(85)90108-8

J. Hosking, J. R. Wallis, and . Wood, Estimation of the Generalized Extreme-Value Distribution by the Method of Probability-Weighted Moments, Technometrics, vol.79, issue.3, pp.251-261, 1985.
DOI : 10.1080/00401706.1985.10488049

T. Hsu and C. Loh, Damage detection accommodating nonlinear environmental effects by nonlinear principal component analysis. Structural Control and Health Monitoring, pp.338-354, 2010.
DOI : 10.1002/stc.320

J. N. Juang and M. Q. Phan, Identification and Control of Mechanical Systems, 2001.

S. Kacimi and S. Laurens, The correlation dimension: A robust chaotic feature for classifying acoustic emission signals generated in construction materials, Journal of Applied Physics, vol.106, issue.2, pp.24909-024909, 2009.
DOI : 10.1063/1.3169601

H. Kantz and T. Schreiber, Nonlinear Time Series Analysis, 2004.
DOI : 10.1017/CBO9780511755798

J. Kaplan and J. Yorke, Chaotic behavior of multidimensional difference equations. Functional Differential equations and approximation of fixed points, pp.204-227, 1979.
DOI : 10.1007/bfb0064319

C. Kim, D. Jung, N. Kim, S. Kwon, and E. M. Feng, Effect of vehicle weight on natural frequencies of bridges measured from traffic-induced vibration, Earthquake Engineering and Engineering Vibration, vol.2, issue.1, pp.109-115, 2003.
DOI : 10.1007/BF02857543

S. Kotz and S. Nadarajah, Extreme value distributions : theory and applications, 2000.
DOI : 10.1142/p191

M. A. Kramer, Nonlinear principal component analysis using autoassociative neural networks, AIChE Journal, vol.37, issue.2, pp.233-243, 1991.
DOI : 10.1002/aic.690370209

C. Krämer, C. A. De, E. G. Smet, and . De-roeck, Z24 bridge damage detection tests, Proceedings of the International Modal Analysis Conference, IMAC XVII, 1999.

B. Lebaron, Chaos and Nonlinear Forecastability in Economics and Finance, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.348, issue.1688, pp.397-404, 1994.
DOI : 10.1098/rsta.1994.0099

H. Li, S. Li, J. Ou, and E. H. Li, Modal identification of bridges under varying environmental conditions : Temperature and wind effects. Structural Control and Health Monitoring, 2009.

R. A. Livingston, S. Jin, and E. D. Marzougui, Application of nonlinear dynamics analysis to damage detection and health monitoring of highway structures Society of Photo-Optical Instrumentation Engineers, Proceedings of SPIE -The International Society for Optical Engineering, pp.402-410, 2001.

D. B. Logan and J. Mathew, USING THE CORRELATION DIMENSION FOR VIBRATION FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS???II. SELECTION OF EXPERIMENTAL PARAMETERS, Mechanical Systems and Signal Processing, vol.10, issue.3, pp.251-264, 1996.
DOI : 10.1006/mssp.1996.0019

E. N. Lorenz, Deterministic Nonperiodic Flow, Journal of the Atmospheric Sciences, vol.20, issue.2, pp.130-141, 1963.
DOI : 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2

Y. Lu and J. E. Michaels, State Space Feature Extraction Applied to Diffuse Ultrasonic Signals Using Simulated Chaotic Excitations, AIP Conference Proceedings, p.625, 2005.
DOI : 10.1063/1.2184585

M. Markou and S. Singh, Novelty detection: a review???part 1: statistical approaches, Signal Processing, vol.83, issue.12, pp.2481-2497
DOI : 10.1016/j.sigpro.2003.07.018

M. Markou and S. Singh, Novelty detection: a review???part 2:, Signal Processing, vol.83, issue.12, pp.2499-2521, 2003.
DOI : 10.1016/j.sigpro.2003.07.019

A. Mita and H. Hagiwara, Quantitative damage diagnosis of shear structures using support vector machine, KSCE Journal of Civil Engineering, vol.56, issue.No. 5, pp.683-689, 2003.
DOI : 10.1007/BF02829138

L. Moniz, J. M. Nichols, C. J. Nichols, M. Seaver, S. T. Trickey et al., A multivariate, attractor-based approach to structural health monitoring, Journal of Sound and Vibration, vol.283, issue.1-2, pp.295-310, 2005.
DOI : 10.1016/j.jsv.2004.04.016

D. C. Montgomery, Introduction to Statistical Quality Control, pp.33-35, 2005.

K. K. Nair, A. S. Kiremidjian, and K. H. Law, Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure, Journal of Sound and Vibration, vol.291, issue.1-2, pp.349-368, 2006.
DOI : 10.1016/j.jsv.2005.06.016

J. M. Nichols, Structural health monitoring of offshore structures using ambient excitation, Applied Ocean Research, vol.25, issue.3, pp.101-114, 2003.
DOI : 10.1016/j.apor.2003.08.003

J. M. Nichols, M. J. Hunther, M. Seaver, and S. I. Trickey, The role of excitation in attractor-based structural health monitoring, Proceedings of the 4th International Workshop on Structural Health Monitoring, pp.47-54, 2003.

J. M. Nichols and J. D. Nichols, Attractor reconstruction for non-linear systems: a methodological note, Mathematical Biosciences, vol.171, issue.1, pp.21-32, 2001.
DOI : 10.1016/S0025-5564(01)00053-0

J. M. Nichols, M. D. Todd, and J. R. Wait, Using state space predictive modeling with chaotic interrogation in detecting joint preload loss in a frame structure experiment, Smart Materials and Structures, vol.12, issue.4, pp.580-601, 2003.
DOI : 10.1088/0964-1726/12/4/310

J. M. Nichols, S. T. Trickey, and E. M. Seaver, Damage detection using multivariate recurrence quantification analysis, Mechanical Systems and Signal Processing, pp.421-437, 2006.
DOI : 10.1016/j.ymssp.2004.08.007

J. M. Nichols, L. N. Virgin, M. D. Todd, and J. D. Nichols, ON THE USE OF ATTRACTOR DIMENSION AS A FEATURE IN STRUCTURAL HEALTH MONITORING, Mechanical Systems and Signal Processing, vol.17, issue.6, pp.1305-1320, 2003.
DOI : 10.1006/mssp.2002.1521

C. K. Oh and H. Sohn, Damage diagnosis under environmental and operational variations using unsupervised support vector machine, Journal of Sound and Vibration, vol.325, issue.1-2, pp.224-239
DOI : 10.1016/j.jsv.2009.03.014

C. K. Oh, H. Sohn, and I. Bae, Statistical novelty detection within the yeongjong suspension bridge under environmental and operational variations . Dans Structural health monitoring (Oh et Sohn, pp.224-239, 2009.

C. C. Olson, L. A. Overbey, and M. D. Todd, <title>Sensitivity and computational comparison of state-space methods for structural health monitoring</title>, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems IV, pp.241-53, 2005.
DOI : 10.1117/12.598894

V. I. Oseledec, A multiplicative ergodic theorem : Lyapunov characteristic numbers for dynamical systems, Trans. Moscow Math. Soc, vol.19, pp.197-231, 1968.

L. A. Overbey, C. C. Olson, and M. D. Todd, A parametric investigation of state-space-based prediction error methods with stochastic excitation for structural health monitoring, Smart Materials and Structures, vol.16, issue.5, pp.1621-1669, 2007.
DOI : 10.1088/0964-1726/16/5/016

H. W. Park and H. Sohn, Parameter estimation of the generalized extreme value distribution for structural health monitoring, Probabilistic Engineering Mechanics, pp.366-376, 0198.

E. Parzen, On estimation of a probability density function and mode. The annals of mathematical statistics, pp.1065-1076, 1962.

L. M. Pecora, H. P. Geffert, S. Boccaletti, D. L. Valladares, and E. T. Carroll, Reconstructing embedding spaces of coupled dynamical systems from multivariate data, Physical Review E. Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, pp.65-68, 2002.

B. Peeters, System identification and damage detection in civil engineering, 2000.

B. Peeters and G. De-roeck, One year monitoring of the z 24-bridge : environmental influences versus damage events, PROC INT MODAL ANAL CONF IMAC, vol.2, pp.1570-1576, 2000.

B. Peeters and G. De-roeck, Stochastic System Identification for Operational Modal Analysis: A Review, Journal of Dynamic Systems, Measurement, and Control, vol.123, issue.4, pp.659-667, 2001.
DOI : 10.1115/1.1410370

M. Piera-martinez, Modélisation des comportements extrêmes en ingénierie, EVS, 2008.

M. Pillet, Appliquer la maîtrise statistique des processus MSP/SPC, 2008.

A. Rytter, Vibration based inspection of civil engineering structures, pp.10-16, 1993.

M. Sano and Y. Sawada, Measurement of the Lyapunov Spectrum from a Chaotic Time Series, Physical Review Letters, vol.55, issue.10, pp.1082-1085, 1985.
DOI : 10.1103/PhysRevLett.55.1082

B. Schölkopf, A. Smola, and K. R. Müller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998.
DOI : 10.1007/BF02281970

B. Schölkopf and A. J. Smola, Learning with kernels : support vector machines, regularization, optimization, and beyond, 2002.

B. Scölkopf, R. C. Williamson, A. J. Smola, J. Shawe-taylor, and E. J. Platt, Support vector method for novelty detection Advances in neural information processing systems, pp.582-588, 2000.

D. W. Scott and S. R. Sain, Multidimensional Density Estimation, Handbook of Statistics, vol.24, pp.229-261, 2005.
DOI : 10.1016/S0169-7161(04)24009-3

A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Statistics and Computing, vol.14, issue.3, pp.199-222, 2004.
DOI : 10.1023/B:STCO.0000035301.49549.88

H. Sohn, Effects of environmental and operational variability on structural health monitoring. A Special Issue of Philosophical Transaction of, Royal Society A on Structural Health Monitoring, vol.365, pp.539-560, 2007.

H. Sohn, D. W. Allen, K. Worden, and C. R. Farrar, Statistical Damage Classification Using Sequential Probability Ratio Tests, Structural Health Monitoring, vol.2, issue.1, pp.57-74
DOI : 10.1177/147592103031113

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.520.4611

H. Sohn and C. R. Farrar, Statistical process control and projection techniques for damage detection, Proceedings of European COST F3 Conference on System Identification and Structural Health Monitoring, pp.105-114, 2000.

H. Sohn and C. R. Farrar, Damage diagnosis using time series analysis of vibration signals, Smart Materials and Structures, vol.10, issue.3, pp.446-451, 2001.
DOI : 10.1088/0964-1726/10/3/304

H. Sohn, K. Worden, and C. R. Farrar, Statistical Damage Classification Under Changing Environmental and Operational Conditions, Journal of Intelligent Material Systems and Structures, vol.13, issue.9, pp.561-574, 2002.
DOI : 10.1106/104538902030904

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.7551

H. Sohn, D. W. Allen, K. Worden, and C. R. Farrar, Structural Damage Classification Using Extreme Value Statistics, Journal of Dynamic Systems, Measurement, and Control, vol.127, issue.1, pp.125-132, 2006.
DOI : 10.1115/1.1849240

URL : http://koasas.kaist.ac.kr/bitstream/10203/18813/1/J%20DYN%20SYST-T%20ASME_2005_No.28_Structural%20Damage%20Classification%20Using%20Extreme%20Value%20Statistics.pdf

Z. Sun, Structural Damage Assessment Based on Wavelet Packet Transform, Journal of Structural Engineering, vol.128, issue.10, p.1354, 2002.
DOI : 10.1061/(ASCE)0733-9445(2002)128:10(1354)

M. M. Taha, A. Noureldin, J. L. Lucero, and T. J. Baca, Wavelet Transform for Structural Health Monitoring: A Compendium of Uses and Features, Structural Health Monitoring, vol.5, issue.3, pp.267-296, 2006.
DOI : 10.1177/1475921706067741

F. Takens, Detecting strange attractors in turbulence Dynamical systems and turbulence, pp.366-381, 1980.

D. M. Tax, One-class classification. phd, 2001.

J. Theiler, Spurious dimension from correlation algorithms applied to limited time-series data, Physical Review A, vol.34, issue.3, pp.2427-2476, 1986.
DOI : 10.1103/PhysRevA.34.2427

M. Todd, J. Nichols, M. Bement, C. R. Farrar, and E. B. Bakers, Experimental demonstration of local attractor variance as a damage indication feature, Proceedings of SPIE -The International Society for Optical Engineering The International Society for Optical Engineering, pp.1292-1298, 2002.

M. D. Todd, K. Erickson, L. Chang, K. Lee, and J. M. Nichols, Using chaotic interrogation and attractor nonlinear cross-prediction error to detect fastener preload loss in an aluminum frame, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol.14, issue.2, pp.387-399, 2004.
DOI : 10.1063/1.1688091

M. D. Todd, J. M. Nichols, L. M. Pecora, and L. N. Virgin, Vibration-based damage assessment utilizing state space geometry changes: local attractor variance ratio, Smart Materials and Structures, vol.10, issue.5, pp.1000-1008, 2001.
DOI : 10.1088/0964-1726/10/5/316

J. Toivola, M. Prada, and E. J. Hollmén, Novelty detection in projected spaces for structural health monitoring Advances in Intelligent Data Analysis IX, pp.208-219

C. H. Tsou, A statistical learning framework for data mining of large-scale systems : algorithms, implementation, and applications, pp.29-32, 2007.

W. H. Woodall, Controversies and contradictions in statistical process control, Journal of Quality Technology, vol.32, issue.4, pp.341-350, 2000.

K. Worden, W. A. Allen, H. Sohn, W. Stinemates, and C. R. Farrar, Extreme value statistics for damage detection in mechanical structures, evs. Rapport technique, Los Alamos National Lab., NM (United States), 2002.

K. Worden and J. M. Dulieu-barton, An Overview of Intelligent Fault Detection in Systems and Structures, Structural Health Monitoring, vol.3, issue.1, p.85, 2004.
DOI : 10.1177/1475921704041866

K. Worden, C. R. Farrar, G. Manson, and E. G. Park, The fundamental axioms of structural health monitoring, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.463, issue.2082, pp.1639-1654, 2007.
DOI : 10.1098/rspa.2007.1834

K. Worden and G. Manson, The application of machine learning to structural health monitoring, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.259, issue.1851, pp.515-542, 1851.
DOI : 10.1098/rsta.2006.1938

K. Worden, G. Manson, and N. R. Fieller, DAMAGE DETECTION USING OUTLIER ANALYSIS, Journal of Sound and Vibration, vol.229, issue.3, pp.647-667, 2000.
DOI : 10.1006/jsvi.1999.2514

K. Worden, H. Sohn, and C. R. Farrar, NOVELTY DETECTION IN A CHANGING ENVIRONMENT: REGRESSION AND INTERPOLATION APPROACHES, Journal of Sound and Vibration, vol.258, issue.4, pp.741-761, 2002.
DOI : 10.1006/jsvi.2002.5148

A. Yan, G. Kerschen, P. De-boe, and J. Golinval, Structural damage diagnosis under varying environmental conditions???Part I: A linear analysis, Mechanical Systems and Signal Processing, vol.19, issue.4, pp.847-864, 2005.
DOI : 10.1016/j.ymssp.2004.12.002

J. Yang, Y. Zhang, and E. Y. Zhu, Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension, Mechanical Systems and Signal Processing, pp.2012-2024, 2007.
DOI : 10.1016/j.ymssp.2006.10.005

G. G. Yen and K. C. Lin, Wavelet packet feature extraction for vibration monitoring, IEEE Transactions on Industrial Electronics, vol.47, issue.3, pp.650-667, 2000.
DOI : 10.1109/41.847906

J. L. Zapico-valle, M. García-diéguez, M. P. González-martínez, and E. K. Worden, Experimental validation of a new statistical process control feature for damage detection, Mechanical Systems and Signal Processing, pp.2011-2050
DOI : 10.1016/j.ymssp.2011.02.007