\شماره٪٪۱
Jardine, A., Lin, D. and Banjevic, D. ``A review on machinery
diagnostics and prognostics implementing condition-based maintenance'',
{\it Mechanical Systems and Signal Processing}, {\bf 20}(7), pp.
1483-1510 (2006).
\شماره٪٪۲
Singleton, R., Strangas, E. and Aviyente, S. ``Extended
kalman filtering for remaining-useful-life estimation of bearings'', {\it IEEE
Transactions on Industrial Electronics}, {\bf 62}(3), pp. 1781-1790 (2015).
\شماره٪٪۳
Lei, Y., Li, N., Guo, L. and et al. ``Machinery
health prognostics: a systematic review from data acquisition
to RUL prediction'', {\it Mechanical Systems and Signal Processing},
{\bf 104}, pp. 799-834 (2018).
\شماره٪٪۴
Cubillo, A., Perinpanayagam, S. and Esperon-Miguez, M. ``A review
of physics-based models in prognostics: application to gears
and bearings of rotating machinery'', {\it Advances in Mechanical Engineering},
{\bf 8}(8), p. 168781401666466 (2016).
\شماره٪٪۵
Behzad, M., Arghand, H. and Rohani Bastami, A. ``Remaining useful
life prediction of ball-bearings based on high-frequency vibration
features'', {\it Proceedings of the Institution of Mechanical Engineers,
Part C: Journal of Mechanical Engineering Science}, {\bf 232}
(18), pp. 3224-3234 (2017).
\شماره٪٪۶
Mahamad, A., Saon, S. and Hiyama, T. ``Predicting remaining
useful life of rotating machinery based artificial neural network'',
{\it Computers}
\& {\it Mathematics With Applications}, {\bf 60}(4), pp. 1078-1087 (2010).
\شماره٪٪۷
Li, X., Elasha, F., Shanbr, S. and et al. ``Remaining useful
life prediction of rolling element bearings using supervised
machine learning'', {\it Energies}, {\bf 12}(14), p. 2705 (2019).
\شماره٪٪۸
Peng, C., Chen, Y., Chen, Q. and et al.
``A Remaining useful life prognosis of turbofan engine using
temporal and spatial feature fusion'', {\it Sensors}, {\bf 21}(2), p. 418
(2021).
\شماره٪٪۹
Zhai, Y., Deng, A., Li, J. and et al. ``Remaining
useful life prediction of rolling bearings based on recurrent
neural network'', {\it Journal on Artificial Intelligence},
{\bf 1}(1), pp. 19-27 (2019).
\شماره٪٪۱۰
Mao, W., He, J., Tang, J. and et al. ``Predicting remaining
useful life of rolling bearings based on deep feature representation
and long short-term memory neural network'', {\it Advances in Mechanical
Engineering}, {\bf 10}(12), p. 168781401881718 (2018).
\شماره٪٪۱۱
Yang, C., Chen, Y., Chan, Y. and et al.
``Influenza-like illness prediction using a
long short-term memory deep learning model with multiple open
data sources'', {\it The Journal of Supercomputing},
{\bf 76}(12), pp. 9303-9329 (2020).
\شماره٪٪۱۲
Y{\i}ld{\i}r{\i}m, D., Toroslu, I. and Fiore, U. ``Forecasting
directional movement of forex data using LSTM with technical
and macroeconomic indicators'', {\it Financial Innovation}, {\bf 7}(1) (2021).
\شماره٪٪۱۳
Song, X., Liu, Y., Xue, L. and et al.
``Time-series well performance prediction
based on long short-term memory (LSTM) neural network model'',
{\it Journal of Petroleum Science and Engineering}, {\bf 186}, p. 106682
(2020).
\شماره٪٪۱۴
Reuben, L. and Mba, D. ``Diagnostics and prognostics
using switching Kalman filters'', {\it Structural Health Monitoring},
{\bf 13}(3), pp. 296-306 (2014).
\شماره٪٪۱۵
Heimes, F. ``Recurrent neural networks for remaining useful
life estimation'', {\it International Conference on Prognostics
and Health Management} (2008).
\شماره٪٪۱۶
Zhai, Y., Deng, A., Li, J. and et al. ``Remaining
useful life prediction of rolling bearings based on recurrent
neural network'', {\it Journal on Artificial Intelligence}, {\bf 1}
(1), pp. 19-27 (2019).
\شماره٪٪۱۷
Kulkarni, S. and Wadkar, S. ``Experimental investigation
for distributed defects in ball bearing using vibration signature
analysis'', {\it Procedia Engineering}, {\bf 144}, pp. 781-789 (2016).
\شماره٪٪۱۸
Behzad, M., Feizhoseini, S., Arghand, H. and et al.
``Failure threshold determination of rolling
element bearings using vibration fluctuation and failure modes'',
{\it Applied Sciences}, {\bf 11}(1), p. 160 (2020).
\شماره٪٪۱۹
Hamadache, M., Jung, J., Park, J. and et al. ``A comprehensive
review of artificial intelligence-based approaches for rolling
element bearing PHM: shallow and deep learning'', {\it JMST Advances},
{\bf 1}(1-2), pp. 125-151 (2019).
\شماره٪٪۲۰
Medjaher, K., Tobon-Mejia, D. and Zerhouni, N.
``Remaining
useful life estimation of critical components with application
to bearings'', {\it IEEE Transactions on Reliability, Institute of Electrical
and Electronics Engineers},
{\bf 61}(2), pp. 292-302 10.1109/TR.2012.2194175.
hal-00737596 (2012).
\شماره٪٪۲۱
Feng, C., Wang, H., Lu, N. and et al. ``Log-transformation
and its implications for data analysis'', {\it Shanghai Arch. Psychiatry},
{\bf 26}(2), pp.105-109 (2014). DOI: 10.3969/j.issn.1002-0829.2014.02.
\شماره٪٪۲۲
Jebb, A.T., Tay, L. Wang, W. and et al.
``Time series analysis for psychological
research: examining and forecasting change'', {\it Front Psychol},
6:727 (2015). DOI: 10.3389/fpsyg.2015.00727.
\شماره٪٪۲۳
Nectoux, P., Gouriveau, R., Medjaher, K. and et al. ``PRONOSTIA: an
experimental platform for bearings accelerated degradation tests'',
{\it In: IEEE International Conference on Prognostics and Health Management},
PHM 12, Denver, CO, USA. IEEE Catalog Number:
CPF12PHM-CDR (June 2012).