J. Reifman, Survey of artificial intelligence methods for detection and identification of component faults in nuclear power plants, Nucl. Technol, vol.119, pp.76-97, 1997.

P. Vaidya and M. Rausand, R m i i g f l lif h i l h l h d lif i " Proceedings of the Institution of Mechanical Engineers, Journal of Risk and Reliability, vol.225, issue.2, pp.219-231, 2011.

P. Baraldi, M. Compare, A. Despujols, and E. Zio, Modeling of the effect of maintenance actions on the degradation of an electric power plant component, Proceedings of the Institution of Mechanical Engineers, pp.169-184, 2011.

P. Baraldi, R. Canesi, E. Zio, R. Seraoui, and R. Chevalier, Signal Grouping for Condition Monitoring of Nuclear Power Plant Components' , Seventh American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies NPIC&HMIT, 2010.

J. W. Hines and D. R. Garvey, Development and Application of Fault Detectability Performance Metrics for Instrument Calibration Verification and Anomaly Detection, Journal of Pattern Recognition Research, vol.1, issue.1, pp.2-15, 2006.
DOI : 10.13176/11.5

G. Strang and T. Nguyen, Wavelets and Filter Banks, 1996.

R. T. Ogden, Essential Wavelets for Statistical Applications and Data Analysis, Birkhausen, 1997.
DOI : 10.1007/978-1-4612-0709-2

M. Vetterli and C. Herley, Wavelets and filter banks: theory and design, IEEE Transactions on Signal Processing, vol.40, issue.9, pp.2207-2231, 1992.
DOI : 10.1109/78.157221

URL : http://infoscience.epfl.ch/record/33904

D. Roverso, Fault diagnosis with the aladdin transient classifier In: Proceedings of System Diagnosis and Prognosis: Security and Condition Monitoring Issues III, AeroSense2003, Aerospace and Defense Sensing and Control Technologies Symposium, pp.21-25, 2003.

D. Roverso, Soft computing tools for transient classification, Information Sciences, vol.127, issue.3-4, pp.137-156, 2000.
DOI : 10.1016/S0020-0255(00)00035-9

P. Baraldi, N. Pedroni, and E. Zio, Application of a niched Pareto genetic algorithm for selecting features for nuclear transients classification, International Journal of Intelligent Systems, vol.23, issue.2, pp.118-151, 2009.
DOI : 10.1002/int.20328

P. Baraldi, R. Canesi, E. Zio, R. Seraoui, and R. Chevalier, Generic algorithm-based wrapper approach for grouping condition monitoring signal of nuclear power plant components, Integrated Computer-Aided Engineering, vol.18, issue.3, pp.221-234, 2011.

K. C. Gross and K. E. Kumenik, Sequential probability ratio test for nuclear power plant component surveillance', Nuclear Technology 93, pp.131-137, 1991.

K. C. Gross and W. Lu, Early Detection of Signal and Process Anomalies in Enterprise Computing Systems, International Conference on Machine Learning and Applications, pp.204-210, 2002.

E. Zio, P. Baraldi, and N. Pedroni, Selecting features for nuclear transients classification by means of genetic algorithms, IEEE Transactions on Nuclear Science, vol.53, issue.3, pp.1479-1493, 2006.
DOI : 10.1109/TNS.2006.873868

B. Efron, Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation, Journal of the American Statistical Association, vol.78, issue.382, pp.316-331, 1983.
DOI : 10.1080/01621459.1983.10477973

B. Efron and R. J. Tibshirani, Improvements on cross-validation: The .632+ bootstrap method, Journal of the American Statistical Association, vol.92, pp.548-560, 1995.

R. Kohavi, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, pp.1137-1143, 1995.

R. Polikar, Bootstrap-Inspired Techniques in Computational Intelligence, IEEE signal processing magazine, vol.59, pp.59-72, 2007.

D. Roverso, Multivariate Temporal Classification by Windowed Wavelet Decomposition and Recurrent Neural Networks, International Topical Meeting on Nuclear Power Plant Instrumentation, Controls and Human-Machine Interface Technologies (NPIC&HMIT2000), 2000.

P. Baraldi, E. Zio, F. Di-maio, L. Pappaglione, R. Chevalier et al., Differential evolution for optimal grouping of condition monitoring signals of nuclear components, Proceedings of ESREL 2011 Conference, pp.410-418, 2011.
DOI : 10.1201/b11433-59

URL : https://hal.archives-ouvertes.fr/hal-00658087

P. Baraldi, E. Zio, G. Gola, D. Roverso, and M. Hoffmann, Genetic algorithms for signal grouping in sensor validation: a comparison of the filter and wrapper approaches, Proceedings of the Institution of Mechanical Engineers, pp.189-206, 2008.
DOI : 10.1243/1748006XJRR137

E. Zio, P. Baraldi, and G. Gola, Ensemble feature selection for diagnosing multiple faults in rotating machinery, Proceedings of the Institution of Mechanical Engineers, pp.29-41, 2007.
DOI : 10.1243/1748006XJRR38