R. Gouriveau, K. Medjaher, and N. Zerhouni, From Prognostics and Health Systems Management to Predictive Maintenance 1: Monitoring and Prognostics, 2016.

V. Atamuradov, K. Medjaher, P. Dersin, B. Lamoureux, and N. Zerhouni, Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation, Int. J. Progn. Health Manage, vol.8, issue.060, pp.1-31, 2017.

A. Soualhi, K. Medjaher, N. Zerhouni, and H. Razik, Early detection of bearing faults by the hilbert-huang transform, Control Engineering & Information Technology (CEIT), pp.1-6, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01899873

R. Shao, W. Hu, Y. Wang, and X. Qi, The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform, Measurement, vol.54, pp.118-132, 2014.

D. Lu, W. Qiao, and X. Gong, Current-based gear fault detection for wind turbine gearboxes, IEEE Trans. Sustainable Energy, vol.8, issue.4, pp.1453-1462, 2017.

Y. Zhang, C. Zhang, J. Sun, and J. Guo, Improved wind speed prediction using empirical mode decomposition, Adv. Electr. Comput. Eng, vol.18, issue.2, pp.3-11, 2018.

Y. Zhang, B. Chen, Y. Zhao, and G. Pan, Wind speed prediction of ipso-bp neural network based on lorenz disturbance, IEEE Access, vol.6, pp.53168-53179, 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 statistical-time features and neural networks, IEEE Trans. Ind. Electron, vol.60, issue.8, pp.3398-3407, 2013.

S. Shukla, R. Yadav, J. Sharma, and S. Khare, Analysis of statistical features for fault detection in ball bearing, Computational Intelligence and Computing Research (ICCIC), 2015 IEEE International Conference on, pp.1-7, 2015.

S. Liu, S. Hou, K. He, and W. Yang, L-kurtosis and its application for fault detection of rolling element bearings, Measurement, vol.116, pp.523-532, 2018.

R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartfield, Motor bearing damage detection using stator current monitoring, IEEE Trans. Ind. Appl, vol.31, issue.6, pp.1274-1279, 1995.

J. R. Stack, T. G. Habetler, and R. G. Harley, Bearing fault detection via autoregressive stator current modeling, IEEE Trans. Ind. Appl, vol.40, issue.3, pp.740-747, 2004.

J. Zhang, J. S. Dhupia, and C. J. Gajanayake, Stator current analysis from electrical machines using resonance residual technique to detect faults in planetary gearboxes, IEEE Trans. Industr. Electron, vol.62, issue.9, pp.5709-5721, 2015.

S. H. Kia, H. Henao, and G. A. Capolino, Fault index statistical study for gear fault detection using stator current space vector analysis, IEEE Trans. Ind. Appl, vol.52, issue.6, pp.4781-4788, 2016.

A. Glowacz and W. Glowacz, Vibration-based fault diagnosis of commutator motor, Shock Vibr, pp.1-10, 2018.

L. S. Dhamande and M. B. Chaudhari, Compound gear-bearing fault feature extraction using statistical features based on time-frequency method, Measurement, vol.125, pp.63-77, 2018.

C. Wang, M. Gan, and C. Zhu, A supervised sparsity-based wavelet feature for bearing fault diagnosis, J. Intell. Manuf, pp.1-11, 2016.

C. Wang, M. Gan, and C. Zhu, Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory, J. Intell. Manuf, vol.29, issue.4, pp.937-951, 2018.

C. Wu, T. Chen, R. Jiang, L. Ning, and Z. Jiang, A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault, J. Intell. Manuf, vol.28, issue.8, pp.1847-1858, 2017.

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

S. Singh and N. Kumar, Detection of bearing faults in mechanical systems using stator current monitoring, IEEE Trans. Ind. Inf, vol.13, issue.3, pp.1341-1349, 2017.

L. Hong and J. S. Dhupia, A time domain approach to diagnose gearbox fault based on measured vibration signals, J. Sound Vib, vol.333, issue.7, pp.2164-2180, 2014.

S. Fedala, D. Rémond, R. Zegadi, and A. Felkaoui, Contribution of angular measurements to intelligent gear faults diagnosis, J. Intell. Manuf, vol.29, issue.5, pp.1115-1131, 2018.

A. Glowacz, Acoustic-based fault diagnosis of commutator motor, Electronics, vol.7, issue.11, p.299, 2018.

A. Glowacz, Recognition of acoustic signals of commutator motors, Appl. Sci, vol.8, issue.12, p.2630, 2018.

A. Glowacz, Fault diagnosis of single-phase induction motor based on acoustic signals, Mech. Syst. Signal Process, vol.117, pp.65-80, 2019.

O. V. Thorsen and M. Dalva, A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals, and oil refineries, IEEE Trans. Ind. Appl, vol.31, issue.5, pp.1186-1196, 1995.

W. T. Thomson and M. Fenger, Current signature analysis to detect induction motor faults, IEEE Ind. Appl. Mag, vol.7, issue.4, pp.26-34, 2001.

O. Ondel, G. Clerc, E. Boutleux, and E. Blanco, Fault detection and diagnosis in a set 'inverter-induction machine' through multidimensional membership function and pattern recognition, IEEE Trans. Energy Convers, vol.24, issue.2, pp.431-441, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00372262

A. Soualhi and S. Taleb, Data fusion for fault severity estimation of ball bearings, IEEE International Conference on Industrial Technology (ICIT), pp.2105-2110, 2018.

P. Konar and P. Chattopadhyay, Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs), Appl. Soft Comput, vol.11, issue.6, pp.4203-4211, 2011.

T. Dalstein and B. Kulicke, Neural network approach to fault classification for high speed protective relaying, IEEE Trans. Power Delivery, vol.10, issue.2, pp.1002-1011, 1995.

T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Trans. Inf. Theory, vol.13, issue.1, pp.21-27, 1967.

S. Abbasion, A. Rafsanjani, A. Farshidianfar, and N. Irani, Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine, Mech. Syst. Signal Process, vol.21, issue.7, pp.2933-2945, 2007.

K. Salahshoor, M. Kordestani, and M. S. Khoshro, Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers, Energy, vol.35, issue.12, pp.5472-5482, 2010.

S. Xu, Bayesian naïve bayes classifiers to text classification, J. Inf. Sci, vol.44, issue.1, pp.48-59, 2018.

J. S. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern, vol.23, issue.3, pp.665-685, 1993.

Y. Lei, Z. He, Y. Zi, and Q. Hu, Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs, Mech. Syst. Signal Process, vol.21, issue.5, pp.2280-2294, 2007.

M. ßahin and R. Erol, A comparative study of neural networks and anfis for forecasting attendance rate of soccer games, Math. Comput. Appl, vol.22, issue.4, p.43, 2017.