J. &. Antoni and R. B. Randall, Differential Diagnosis of Gear and Bearing Faults, Journal of Vibration and Acoustics, vol.124, issue.2, pp.165-171, 2002.
DOI : 10.1115/1.1456906
URL : https://hal.archives-ouvertes.fr/hal-01714460

J. B. Ali, N. Fnaiech, L. Saidi, B. Chebel-morello, and F. Fnaiech, Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals, Applied Acoustics, vol.89, pp.16-27, 2015.

M. P. Appleby, Wear debris detection and oil analysis using ultrasonic and capacitance measurements, 2003.

F. Auger and P. Flandrin, Ctime-frequency toolbox, p.46, 1996.

A. Z. Averbuch and V. A. Zheludev, Lifting scheme for biorthogonal multiwavelets originated from hermite splines, IEEE Transactions on Signal Processing, vol.50, issue.3, pp.487-500, 2002.
DOI : 10.1109/78.984720

T. Barszcz and A. Jablonski, A novel method for the optimal band selection for vibration signal demodulation and comparison with the kurtogram, Mechanical Systems and Signal Processing, vol.25, issue.1, pp.431-451, 2011.

A. Bellini, F. Immovilli, R. Rubini, and C. Tassoni, Diagnosis of bearing faults of induction machines by vibration or current signals: A critical comparison, IEEE Industry Applications Society Annual Meeting, pp.1-8, 2008.

M. Bengtsson, Standardization issues in condition based maintenance, Condition Monitoring and Diagnostic Engineering Management, pp.651-660, 2003.

W. R. Bennett, Statistics of regenerative digital transmission, Bell System Technical Journal, vol.37, issue.6, pp.1501-1542, 1958.
DOI : 10.1002/j.1538-7305.1958.tb01560.x

T. Benkedjouh, N. Zerhouni, and S. Rechak, Tool wear condition monitoring based on continuous wavelet transform and blind source separation. The International, Journal of Advanced Manufacturing Technology, pp.1-13, 2018.
DOI : 10.1007/s00170-018-2018-6

A. Bleakie and D. Djurdjanovic, Feature extraction, condition monitoring, and fault modeling in semiconductor manufacturing systems, Computers in Industry, vol.64, issue.3, pp.203-213, 2013.
DOI : 10.1016/j.compind.2012.10.002

F. Bonnardot, Comparison between angular and time domain analysis of rotating machine vibratory signals. Study of fuzzy-cyclostationnarity concept. PhD thesis, 2004.
URL : https://hal.archives-ouvertes.fr/tel-00103067

L. Burgess and T. Shimbel, What is the prognosis on your maintenance program, In Engineering and Mining Journal, vol.196, pp.32-35, 1995.

H. Cao, F. Fan, K. Zhou, and Z. He, Wheelbearing fault diagnosis of trains using empirical wavelet transform, Measurement, vol.82, pp.439-449, 2016.
DOI : 10.1016/j.measurement.2016.01.023

P. Casoli, A. Bedotti, F. Campanini, and M. Pastori, A Methodology Based on Cyclostationary Analysis for Fault Detection of Hydraulic Axial Piston Pumps, Energies, vol.11, issue.7, pp.1-19, 2018.

K. Chen, X. Li, F. Wang, T. Wang, and C. Wu, Bearing fault diagnosis using wavelet analysis, International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, pp.699-702, 2012.

Y. Dai, Y. Xue, and J. Zhang, A continuous wavelet transform approach for harmonic parameters estimation in the presence of impulsive noise, Journal of Sound and Vibration, vol.360, pp.300-314, 2016.

G. Didier, Modélisation et diagnostic de la machine asynchrone en présence de défaillances, 2004.

V. T. Do and U. Chong, Signal model-based fault detection and diagnosis for induction motors using features of vibration signal in two-dimension domain, Journal of Mechanical Engineering, vol.57, issue.9, pp.655-666, 2011.

J. P. Dron, F. Bolaers, and I. Rasolofondraibe, Improvement of the sensitivity of the scalar indicators (crest factor, kurtosis) using a de-noising method by spectral subtraction: application to the detection of defects in ball bearings, Journal of Sound and Vibration, vol.270, issue.1-2, pp.61-73, 2004.

M. El-badaoui, Contribution to the Vibratory Diagnosis of the Complex gears by the Cepstrum analysis. Theses, 1999.
URL : https://hal.archives-ouvertes.fr/tel-00292655

W. Elghazel, J. Bahi, C. Guyeux, M. Hakem, K. Medjaher et al., Dependability of wireless sensor networks for industrial prognostics and health management, Computers in Industry, vol.68, pp.1-15, 2015.
DOI : 10.1016/j.compind.2014.10.004
URL : https://hal.archives-ouvertes.fr/hal-01301811

Z. Feng and M. Liang, Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time-frequency analysis, vol.66, pp.468-477, 2014.
DOI : 10.1016/j.renene.2013.12.047

K. Feng, K. Wang, M. Zhang, Q. Ni, and M. J. Zuo, A diagnostic signal selection scheme for planetary gearbox vibration monitoring under non-stationary operational conditions, Measurement Science and Technology, vol.28, issue.3, p.35003, 2017.
DOI : 10.1088/1361-6501/aa543e

G. F. Franklin, J. Da-powell, and A. Emami-naeini, Feedback Control of Dynamic Systems, 2010.

V. Giurgiutiu, Y. , and L. , In Comparison of Short-time Fourier Transform and Wavelet Transform of Transient and Tone Burst Wave Propagation Signals For Structural Health Monitoring, pp.1267-1274, 2003.

X. Gong and W. Qiao, Bearing fault diagnosis for direct-drive wind turbines via current-demodulated signals, IEEE Transactions on Industrial Electronics, vol.60, issue.8, pp.3419-3428, 2013.
DOI : 10.1109/tie.2013.2238871
URL : https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1272&context=electricalengineeringfacpub

J. Hammond and P. White, The analysis of nonstationary signals using time-frequency methods, Journal of Sound and Vibration, vol.190, issue.3, pp.419-447, 1996.

J. Han, F. Dong, and Y. Y. Xu, Entropy feature extraction on flow pattern of gas/liquid two-phase flow based on cross-section measurement, Journal of Physics: Conference Series, vol.147, issue.1, p.12041, 2009.
DOI : 10.1088/1742-6596/147/1/012041
URL : http://iopscience.iop.org/article/10.1088/1742-6596/147/1/012041/pdf

F. Hemmati, W. Orfali, and M. S. Gadala, Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation, Applied Acoustics, vol.104, pp.101-118, 2016.
DOI : 10.1016/j.apacoust.2015.11.003

A. Heng, S. Zhang, A. C. Tan, M. , and J. , Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, vol.23, pp.724-739, 2009.
DOI : 10.1016/j.ymssp.2008.06.009

K. Holmberg, A. Adgar, A. Arnaiz, E. Jantunen, J. Mascolo et al., E-maintenance, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00548712

T. J. Holroyd, The application of ae in condition monitoring, International Conference on Condition Monitoring, vol.47, pp.481-485, 2005.

L. Hong and J. S. Dhupia, A time domain approach to diagnose gearbox fault based on measured vibration signals, Journal of Sound and Vibration, vol.333, issue.7, pp.2164-2180, 2014.
DOI : 10.1016/j.jsv.2013.11.033

D. T. Hountalas, Prediction of marine diesel engine performance under fault conditions, Applied Thermal Engineering, vol.20, issue.18, pp.1753-1783, 2000.
DOI : 10.1016/s1359-4311(00)00006-5

M. R. Jaloretto, C. R. De-oliveira, and R. Kawakami, Trend analysis for prognostics and health monitoring, CTA-DLR Workshop on Data Analysis & Flight Contr, pp.14-16, 2009.

P. W. Kalgren, C. S. Byington, M. J. Roemer, and M. J. Watson, Defining phm, a lexical evolution of maintenance and logistics, IEEE Autotestcon, pp.353-358, 2006.
DOI : 10.1109/autest.2006.283685

P. Kankar, S. C. Sharma, and S. Harsha, Rolling element bearing fault diagnosis using wavelet transform, Neurocomputing, vol.74, issue.10, pp.1638-1645, 2011.
DOI : 10.1016/j.neucom.2011.01.021

C. Kar and A. Mohanty, Monitoring gear vibrations through motor current signature analysis and wavelet transform, Mechanical Systems and Signal Processing, vol.20, issue.1, pp.158-187, 2006.
DOI : 10.1016/j.ymssp.2004.07.006

B. Kim, S. Lee, M. Lee, J. Ni, J. Song et al., A comparative study on damage detection in speed-up and coast-down process of grinding spindletyped rotor-bearing system, Journal of Materials Processing Technology, pp.30-36, 2007.

N. H. Kim, D. An, and J. H. Choi, Prognostics and health management of engineering systems, 2017.

N. Kingsbury, The dual-tree complex wavelet transform: A new technique for shift invariance and directional filters, 8th IEEE DSP Workshop, pp.319-322, 1998.

R. Kumar and M. Singh, Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal, Measurement, vol.46, issue.1, pp.537-545, 2013.

S. Kumar, E. Dolev, and M. Pecht, Parameter selection for health monitoring of electronic products. Microelectronics Reliability, vol.50, pp.161-168, 2010.

K. V. Laerhoven, K. A. Aidoo, and S. Lowette, Real-time analysis of data from many sensors with neural networks, Proceedings Fifth International Symposium on Wearable Computers, pp.115-122, 2001.

J. Lee, Sound and vibration signal analysis using improved short-time fourier representation, International Journal of Automotive and Mechanical Engineering, vol.7, pp.811-819, 2013.

Y. Lei, J. Lin, Z. He, and M. J. Zuo, A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mechanical Systems and Signal Processing, vol.35, issue.1, pp.108-126, 2013.

V. C. Leite, J. G. Da-silva, G. F. Veloso, L. E. Da-silva, G. Lambert-torres et al., Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current, IEEE Transactions on Industrial Electronics, vol.62, issue.3, pp.1855-1865, 2015.

B. Liu, S. Ling, and R. Gribonval, Bearing failure detection using matching pursuit, NDT & E International, vol.35, issue.4, pp.255-262, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00576214

M. Li, H. Liu, W. Zhu, Y. , and J. , Applying improved multiscale fuzzy entropy for feature extraction of mi-eeg, Applied Sciences, vol.7, p.92, 2017.

X. Li, X. Wang, M. Rong, D. Xie, N. Yin et al., Comparison of different timefrequency analysis methods for sparse representation of pd-induced uhf signal, 2016 China International Conference on Electricity Distribution (CICED), pp.1-5, 2016.

Y. Li, M. Xu, R. Wang, and W. Huang, A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy, Journal of Sound and Vibration, vol.360, pp.277-299, 2016.

J. Ma, J. Wu, and X. Wang, A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing, Journal of Low Frequency Noise, Vibration and Active Control, 2018.

S. G. 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.

S. G. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.

D. Mba, Development of acoustic emission technology for condition monitoring and diagnosis of rotating machines: Bearings, pumps, gearboxes, engines, and rotating structures, vol.38, pp.3-16, 2006.

F. Millioz and N. Martin, Circularity of the stft and spectral kurtosis for time-frequency segmentation in gaussian environment, IEEE Transactions on Signal Processing, vol.59, issue.2, pp.515-524, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00522190

C. Nagaraju, K. Narayana-rao, and K. Mallikarijuna-rao, Application of 3d wavelet transforms for crack detection in rotor systems, Sadhana, vol.34, issue.3, pp.407-419, 2009.

G. Niu, D. Lau, and M. Pecht, Improving computer manufacturing management through lean six sigma and phm, Prognostics and System Health Management Conference, pp.1-7, 2010.

G. Niu and B. Yang, Intelligent condition monitoring and prognostics system based on data-fusion strategy, Expert Systems with Applications, vol.37, issue.12, pp.8831-8840, 2010.

G. Niu, Data-Driven Technology for Engineering Systems Health Management, 2017.

F. K. Omar and A. Gaouda, Dynamic waveletbased tool for gearbox diagnosis, Mechanical Systems and Signal Processing, vol.26, pp.190-204, 2012.

A. V. Oppenheim and R. W. Schafer, From frequency to quefrency: a history of the cepstrum, IEEE Signal Processing Magazine, vol.21, issue.5, pp.95-106, 2004.

H. Ozturk, M. Sabuncu, Y. , and I. , Early detection of pitting damage in gears using mean frequency of scalogram, Journal of Vibration and Control, vol.14, issue.4, pp.469-484, 2008.

B. Pang, G. Tang, T. Tian, and C. Zhou, Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform, Sensors, vol.18, issue.4, p.1203, 2018.

C. Park, D. Looney, M. M. Hulle, and D. P. Mandic, The complex local mean decomposition, Neurocomputing, vol.74, issue.6, pp.867-875, 2011.

C. Peeters, P. Guillaume, and J. Helsen, Vibrationbased bearing fault detection for operations and maintenance cost reduction in wind energy, Renewable Energy, vol.116, pp.74-87, 2018.

M. D. Prieto, G. Cirrincione, A. G. Espinosa, J. A. Ortega, and H. Henao, Bearing fault detection by a novel condition-monitoring scheme based on statisticaltime features and neural networks, IEEE Transactions on Industrial Electronics, vol.60, issue.8, pp.3398-3407, 2013.

Q. Zhu, Y. W. Shen, and G. , Research and comparison of time-frequency techniques for nonstationary signals, Journal of computers, vol.7, issue.4, pp.954-958, 2012.

J. Rafiee and P. Tse, Use of autocorrelation of wavelet coefficients for fault diagnosis. Mechanical Systems and Signal Processing, vol.23, pp.1554-1572, 2009.

I. Rasovska, B. Chebel-morello, and N. Zerhouni, Classification des différentes architectures en maintenance, In International congress on electrical engineering, vol.23, pp.1-12, 2007.

M. J. Roemer and G. J. Kacprzynski, Advanced diagnostics and prognostics for gas turbine engine risk assessment, IEEE Aerospace Conference, vol.6, pp.345-353, 2000.

L. Saidi, F. Fnaiech, G. A. Capolino, and H. Henao, Stator current bi-spectrum patterns for induction machines multiple-faults detection, IECON 2012 -38th Annual Conference on IEEE Industrial Electronics Society, pp.5132-5137, 2012.

N. Sawalhi, Rolling element bearings: Diagnostic, prognostic and fault simulations. Theses, 2007.

R. Schmidt, Multiple emitter location and signal parameter estimation, IEEE Transactions on Antennas and Propagation, vol.34, issue.3, pp.276-280, 1986.

G. Serbes, H. O. Gulcur, and N. Aydin, Directional dual-tree complex wavelet packet transforms for processing quadrature signals, Medical & Biological Engineering & Computing, vol.54, issue.2, pp.295-313, 2016.

O. R. Seryasat, M. A. Shoorehdeli, F. Honarvar, and A. Rahmani, Multi-fault diagnosis of ball bearing based on features extracted from time-domain and multiclass support vector machine (msvm), IEEE International Conference on Systems, Man and Cybernetics, pp.4300-4303, 2010.

A. Soualhi, K. Medjaher, and N. Zerhouni, Bearing health monitoring based on hilbert huang transform, support vector machine, and regression, IEEE Transactions on Instrumentation and Measurement, vol.64, issue.1, pp.52-62, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01026491

A. Soualhi, H. Razik, G. Clerc, and D. D. Doan, Prognosis of bearing failures using hidden markov models and the adaptive neuro-fuzzy inference system, IEEE Transactions on Industrial Electronics, vol.61, issue.6, pp.2864-2874, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00855283

A. G. Starr, A structured approach to the selection of condition based maintenance, Fifth International Conference on Factory 2000 -The Technology Exploitation Process, pp.131-138, 1997.

K. Swearingen, W. Majkowski, B. Bruggeman, D. Gilbertson, J. Dunsdon et al., An open system architecture for condition based maintenance overview, IEEE Aerospace Conference, pp.1-8, 2007.

W. Sweldens, The lifting scheme: A construction of second generation wavelets, vol.29, pp.511-546, 1998.

N. Tandon and A. Choudhury, A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Tribology International, vol.32, issue.8, pp.469-480, 1999.

M. Thomas, Fiabilité, maintenance prédictive et vibration des machines, 2002.

M. Thurston, An open standard for web-based condition-based maintenance systems, Annual Maintenance and Reliability Conference, pp.401-415, 2001.

M. G. Thurston, An open standard for web-based condition-based maintenance systems, IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference, pp.401-415, 2001.

D. Tobon-mejia, K. Medjaher, and N. Zerhouni, Cnc machine tool's wear diagnostic and prognostic by using dynamic bayesian networks, Mechanical Systems and Signal Processing, vol.28, pp.167-182, 2012.

I. Tsoumas, E. Mitronikas, G. Georgoulas, and A. Safacas, A comparative study of induction motor current signature analysis techniques for mechanical faults detection, 5th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pp.1-6, 2005.

K. Tsui, N. Chen, Q. Zhou, Y. Hai, W. et al., Prognostics and health management: A review on data driven approaches, vol.2015, pp.1-17, 2015.

T. J. Walter and H. Lee, Development of a smart wireless sensor for predicting bearing remaining useful life, 58th Meeting of the society for machinery failure prevention technology, p.77, 2004.

S. Wang, W. Pentney, A. Popescu, T. Choudhury, and M. Philipose, Common sense based joint training of human activity recognizers, Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp.2237-2242, 2007.

W. Wang and P. Mcfadden, Application of wavelets to gearbox vibration signals for fault detection, Journal of Sound and Vibration, vol.192, issue.5, pp.927-939, 1996.

X. Wang, V. Makis, Y. , and M. , A wavelet approach to fault diagnosis of a gearbox under varying load conditions, Journal of Sound and Vibration, vol.329, issue.9, pp.1570-1585, 2010.

Y. Wang, Z. He, and Y. Zi, Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform, Mechanical Systems and Signal Processing, vol.24, issue.1, pp.119-137, 2010.

Y. Wang, Q. Ma, Q. Zhu, X. Liu, and L. Zhao, An intelligent approach for engine fault diagnosis based on hilbert-huang transform and support vector machine, Applied Acoustics, vol.75, pp.1-9, 2014.

R. C. Yam, P. Tse, L. Li, and P. Tu, Intelligent predictive decision support system for condition-based maintenance, The International Journal of Advanced Manufacturing Technology, vol.17, issue.5, pp.383-391, 2001.

R. Yan, R. X. Gao, C. , and X. , Wavelets for fault diagnosis of rotary machines: A review with applications, Signal Processing, vol.96, pp.1-15, 2014.

Y. Yang and S. Nagarajaiah, Blind identification of damage in time-varying systems using independent component analysis with wavelet transform. Mechanical Systems and Signal Processing, vol.47, pp.3-20, 2014.

L. Yu, D. Cleary, M. Osborn, and V. Rajiv, Information fusion strategy for aircraft engine health management, Power for Land, Sea, and Air, vol.1, pp.531-538, 2007.

J. Yuan, Z. He, and Y. Zi, Gear fault detection using customized multiwavelet lifting schemes. Mechanical Systems and Signal Processing, vol.24, pp.1509-1528, 2010.