[1] Kim, N. H., An, D., and Choi, J. H., "Prognostics and Health Management of Engineering
Systems", An Introduction, Springer, Bern, Switzerland, (2016).
[2] Rai, A., and Upadhyay, S. H., "A Review on Signal Processing Techniques Utilized in the
Fault Diagnosis of Rolling Element Bearings", Tribology International, Vol. 96, pp. 289-
306, Poitiers, France, (2016).
[3] Jammu, N.S., and Kankar, P.K., "A Review on Prognosis of Rolling Element Bearings",
International Journal of Engineering Science and Technology, Vol. 3, No. 10, pp. 7497-
7503, Karabük, Turkey, (2011).
[4] Si, X. S., Zhang, Z. X., and Hu, C. H., "Data-Driven Remaining Useful Life Prognosis
Techniques", National Defense Industry Press and Springer-Verlag GmbH, Beijing, China
(2017).
[5] Peng, Y., Dong, M., and Zuo, M.J., "Current Status of Machine Prognostics in Condition-
Based Maintenance: A Review", The International Journal of Advanced Manufacturing
Technology, Vol. 50, pp. 297-313, (2010).
[6] Lei, Y., Li, N., Guo, L., Li, N., Yan, T., and Lin, J., "Machinery Health Prognostics: A
Systematic Review from Data Acquisition to RUL Prediction", Mechanical Systems and
Signal Processing, Vol. 104, pp. 799-834, (2018).
[7] Vachtsevanos, G., and Wang, P., "Fault Prognosis using Dynamic Wavelet Neural
Networks", IEEE Systems Readiness Technology Conference, pp. 857-870, Philadelphia,
USA, (2001)
[8] Cui, Q., Li, Z., Yang, J., and Liang, B., "Rolling Bearing Fault Prognosis using Recurrent
Neural Network", Chinese Control and Decision Conference, pp. 1196-1201, China,
(2017).
[9] Gebraeel, N., Lawley, M., Liu, R., and Parmeshwaran, V., "Residual Life Predictions from
Vibration-based Degradation Signals: A Neural Network Approach", IEEE Transactions on
Industrial Electronics, Vol. 51, pp. 694-700, (2004).
[10] Tian, Z., "An Artificial Neural Network Method for RUL Prediction of Equipment Subject
to Condition Monitoring", Journal of Intelligent Manufacturing, Vol. 23, pp. 227-237,
(2012).
[11] Saon, S., and Hiyama, T., "Predicting Remaining Useful Life of Rotating Machinery Based
Artificial Neural Network", Computers & Mathematics with Applications, Vol. 60, pp.
1078-1087, (2010).
[12] Chen, X., Shen, Z., He, Z., Sun, C., and Liu, Z., "Remaining Life Prognostics of Rolling
Bearing Based on Relative Features and Multivariable Support Vector Machine", Journal
of Mechanical Engineering Science, Vol. 227, pp. 2849-2860, (2013).
[13] Zhang, S., and Ganesan, R., "Multivariable Trend Analysis using Neural Networks for
Intelligent Diagnostics of Rotating Machinery", Journal of Engineering for Gas Turbines
and Power, Vol. 119, No. 2, pp. 378-384, (1997).
[14] Wang, W. Q., Golnaraghi, M. F., and Ismail, F., "Prognosis of Machine Health Condition
using Neuro-fuzzy Systems", Mechanical Systems and Signal Processing, Vol. 18, No. 4,
pp. 813-831, Liverpool, England, (2004).
[15] Behzad, M., Arghand, H. A., and Bastami, A. R., "Rolling Element Bearings Prognostics
using High-frequency Spectrum of Offline Vibration Condition Monitoring Data", 31th
Conference Condition Monitoring and Diagnostic Engineering Management, Manchester,
England, (2018).
[16] Behzad, M., Arghand, H. A., and Rohani Bastami, A., "Remaining Useful Life Prediction
of Ball-bearings Based on High-Frequency Vibration Features", Journal of Mechanical
Engineering Science, Vol. 232, No. 18, pp. 3224-3234, (2018).
[17] Gebraeel, N. Z., and Lawley, M. A., "A Neural Network Degradation Model for
Computing and Updating Residual Life Distributions", IEEE Transactions on Automation
Science and Engineering, Vol. 5, No. 1, pp. 154-163, (2008).
[18] Nectoux, P., Gouriveau, R., and Medjaher, K., "PRONOSTIA: An Experimental Platform
for Bearings Accelerated Degradation Tests", IEEE International Conference on
Prognostics and Health Management, Colorado, United States, (2012).