M. Overview and .. , 21 2.3 Mechanical overview for AS332L2, part 1, 22 2.4 Mechanical overview for AS332L2, part 2. . . . . . . . . . . . 23 2.5 AS332L2 left hand ancillary intermediate gear acquisition. . . 24 2.6 AS332L2 left hand ancillary intermediate gear acquisition, p.24

N. Radial, 137 148 LIST OF FIGURES List of Tables 3.1 Common condition indicators, p.39

.. Test, 89 6.5 Progression analysis method comparison chart, p.113

L. Of and . Bibliography, 1] CIVIL AVIATION AUTHORITY. Cap693, acceptable means of compliance , helicopter health monitoring, 1999.

L. Stuart and B. , Aircraft health and usage monitoring system, 1991.

E. Bechhoefer and P. F. Andreas, Hums optimal weighting of condition indicators to determine the health of a component, American Helicopter Society 60th Annual Forum, 2004.

J. S. Bendat and A. G. Piersol, Random Data-Analysis and Measurement Procedures, Journal of Vibration Acoustics Stress and Reliability in Design, vol.111, issue.3, 1986.
DOI : 10.1115/1.3269865

S. Charbonnier, C. Garcia-beltan, C. Cadet, and S. Gentil, Trends extraction and analysis for complex system monitoring and decision support, Engineering Applications of Artificial Intelligence, vol.18, issue.1, pp.21-36, 2005.
DOI : 10.1016/j.engappai.2004.08.023
URL : https://hal.archives-ouvertes.fr/hal-00368501

S. Chen, C. F. Cowan, and P. M. Grant, Orthogonal least squares learning algorithm for radial basis function networks, IEEE Transactions on Neural Networks, vol.2, issue.2, 1991.
DOI : 10.1109/72.80341

R. L. Claypoole, R. G. Baraniuk, and R. D. Nowak, Adaptive wavelet transforms via lifting, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 1998.
DOI : 10.1109/ICASSP.1998.681737
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.226.7686

R. R. Coifman and D. L. Donoho, Translation-Invariant De-Noising, To Appear, Wavelets and Statistics, 1995.
DOI : 10.1007/978-1-4612-2544-7_9

J. Colomer, J. Melendez, and F. Gamero, QUALITATIVE REPRESENTATION OF PROCESS TRENDS FOR SITUATION ASSESSMENT BASED ON CASES, IFAC World Congress, 2002.
DOI : 10.3182/20020721-6-ES-1901.00923

H. J. Decker and D. G. Lewick, Spiral bevel pinion crack detection in a helicopter gearbox, American Helicopter Society's 59th Annual Forum, 2003.

L. David and . Donoho, De-noising by soft-thresholding, IEEE Transactions on Information Theory, vol.41, issue.3, pp.613-627, 1995.

M. A. Essawy, S. Diwakar, S. Zein-sabatto, and A. K. Garga, Fault diagnosis of helicopter gearboxes using neurofuzzy techniques, 52nd Meeting of the Society for Machinery Failure Prevention Technology (MFPT), 1998.

W. Gersch, T. Brotherton, and S. Braun, Nearest Neighbor-Time Series Analysis Classification of Faults in Rotating Machinery, Journal of Vibration Acoustics Stress and Reliability in Design, vol.105, issue.2, pp.178-184, 1983.
DOI : 10.1115/1.3269082

D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1989.

T. Harris and A. Kohonen, S.o.m. based machine health monitoring system which enables diagnosis of faults not seen in the training set, The 1993 International Joint Conference on Neural Networks, 1993.
URL : https://hal.archives-ouvertes.fr/in2p3-00003401

J. J. Zakrajsek, H. J. Decker, and R. F. Handschuh, An enhancement to the na4 gear vibration diagnostic parameter, technical report nasa tm-106553, arl-tr-389, 1994.

A. J. Hoffman and N. T. Van-der-merwe, The application of neural networks to vibrational diagnostics for multiple fault conditions, Computer Standards & Interfaces, vol.24, issue.2, pp.139-149, 2002.
DOI : 10.1016/S0920-5489(02)00014-4

I. Daubechies, Ten Lectures on Wavelets, 1992.

G. Kitagawa and W. Gersch, A smoothness priors time-varying AR coefficient modeling of nonstationary covariance time series, IEEE Transactions on Automatic Control, vol.30, issue.1, pp.48-56, 1985.
DOI : 10.1109/TAC.1985.1103788

R. Klein, Method and system for diagnostics and prognostics of a mechanical system, 2004.

B. D. Larder, R. Callen, and R. Salter, Intelligent management of hums data: Development of a vibration health monitoring anomaly detection system, American Helicopter Society 62th Annual Forum, 2006.

W. Lewis, C. Perry, and J. Keller, Airworthiness releases as a result of condition based maintenance, 32nd European Rotorcraft Forum, 2006.

J. E. Lopez, I. A. Farber-yeldman, K. A. Oliver, and M. W. Protz, Hierarchical neural networks for improved fault detection using multiple sensors, 1996.

P. Lu, M. Zhang, T. Hsu, and J. Zhang, An evaluation of engine faults diagnostics using artificial neural networks, Journal of Engineering for Gas Turbines and Power, vol.123, 2001.

J. Ma, Energy operator and other demodulation approaches to gear defect detection, Proceedings of the 49th Meeting of the Society for Machinery Failure Prevention Technology (MFPT), 1995.

S. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.7, pp.674-693, 1989.
DOI : 10.1109/34.192463

H. R. Martin, Statistical moment analysis as a means of surface damage detection, Seventh International Modal Analysis Conference, 1989.

M. Gary and . Mcbrien, Vibration monitoring system for gas turbine engines, 2003.

P. D. Mcfadden, Examination of a technique for the early detection of failure in gears by signal processing of the time domain average of the meshing vibration, Mechanical Systems and Signal Processing, vol.1, issue.2, pp.173-183, 1987.
DOI : 10.1016/0888-3270(87)90069-0

P. D. Mcfadden, A technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox, Journal of Sound and Vibration, vol.144, issue.1, pp.163-172, 1991.
DOI : 10.1016/0022-460X(91)90739-7

P. D. Mcfadden and I. Howard, The detection of seeded faults in an epicyclic gearbox by signal averaging of the vibration, p.183, 1990.

E. Mermoz, Automatisation du Diagnostic Vibratoire des Transmissions de Puissance d'Helicoptere, Ecole Nationale Supérieure d'Arts et Métiers, 2002.

J. Moré and D. Sorensen, Computing a Trust Region Step, SIAM Journal on Scientific and Statistical Computing, vol.4, issue.3, pp.553-572, 1983.
DOI : 10.1137/0904038

H. Ocak and K. A. Loparo, Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data, Mechanical Systems and Signal Processing, vol.18, issue.3, pp.515-533, 2004.
DOI : 10.1016/S0888-3270(03)00052-9

J. G. Proakis and D. G. Manolakis, Digital signal processing, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.23, issue.4, 1996.
DOI : 10.1109/TASSP.1975.1162707

J. Kørte, An approach to management of health monitoring applications, Journal of Quality in Maintenance and Engineering, 2002.

J. Kørte and T. Aven, Health monitoring of helicopters -a framework for decision making based on health information, Foresight and Precaution (Proceedings of Esrel, 2000.

B. Samanta and K. R. , ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES, Mechanical Systems and Signal Processing, pp.317-328, 2003.
DOI : 10.1006/mssp.2001.1462

P. D. Samuel and D. J. Pines, Planetary gear box diagnostics using adaptive vibration signal representations: A proposed methodology, American Helicopter Society 57th Annual Forum, 2001.

P. D. Samuel and D. J. Pines, Helicopter transmission diagnostics using constrained adaptive lifting, American Helicopter Society 59th Annual Forum, 2003.

R. M. Stewart, Some useful analysis techniques for gearbox diagnostics, technical report mhm/r/10/77, Machine Health Monitoring Group, 1977.

R. M. Stewart, Advanced transmission system health monitoring (surface space technology), European Helicopter Association Conference, 1987.

N. S. Swansson, Application of vibration signal analysis techniques to health monitoring, Conference on Friction and Wear in Engineering, 1980.

W. Sweldens, The Lifting Scheme: A Custom-Design Construction of Biorthogonal Wavelets, Applied and Computational Harmonic Analysis, vol.3, issue.2, pp.186-200, 1996.
DOI : 10.1006/acha.1996.0015

B. Tran, A rapid helicopter drive train fault detection using adaptivenetwork-based fuzzy method, 32nd European Rotorcraft Forum, 2006.

G. Wackers and J. Kørte, Drift an vulnerability in a complex technical system: Reliability of condition monitoring systems in north sea offshore helicopter transport, Internal Journal of Engineering Education, 2003.

J. Wiig, Methods for parametric trend analysis, JDL6'06, 2006.

J. Wiig, Structuring and communication of hums data, 32nd European Rotorcraft Forum, 2006.

J. Wiig and H. Noura, Flight stage normalization of hums indicators, American Helicopter Society's 62nd Annual Forum, 2006.

J. Wiig, H. Noura, D. Brun-picard, and J. Anifrani, Trend analysis of helicopter condition monitoring indicators, 2006 American Control Conference, 2006.
DOI : 10.1109/ACC.2006.1657334

J. Wiig, H. Noura, D. Brun-picard, and J. Derain, Trend-Based Transmission System Diagnosis, Proceedings of the 45th IEEE Conference on Decision and Control, 2006.
DOI : 10.1109/CDC.2006.377234

J. J. Zakrajsek, A review of transmission diagnostics research at nasa lewis research center, technical report nasa tm-106746, arl-tr-599, 1994.

J. J. Zakrajsek, Transmission diagnostics research at nasa lewis research center, technical report nasa tm-106901, arl-tr-748, 1994.

J. J. Zakrajsek, D. P. Townsend, and H. J. Decker, An analysis of gear fault detection methods as applied to pitting fatigue failure data, technical report nasa tm-105950, avscom tr-92-c-035, NASA and the US Army Aviation Systems Command, 1993.