A Systematic Review of Remaining Useful Life Prediction in Roller Bearings Using Artificial Intelligence Techniques
Abstract
Artificial Intelligence (AI) has demonstrated to be an effective method for predicting irregularities across various Industrial processes. In recent years, the development of predictive maintenance systems using AI techniques has attracted many researchers around the world. Maintenance planning has been effective with Remaining useful life prediction using AI in bearings. This review brings consideration to the role of AI in predicting the remaining useful life (RUL) of bearing components in various industrial processes. A systematic search was carried out in electronic databases such as Springer, IEEE, Elsevier, and the ACM Digital Library, with an emphasis on AI-based approaches for bearing RUL prediction. A brief summary of previous works is presented to show the development of technological advancement in this field of RUL prediction of roller bearing. Specifically, this review examines the types of bearing components studied, the sample sizes used for training AI models, the signal processing method and classification algorithm applied, and the outcomes achieved. The outcome of this review shows that hybrid approaches and deep learning models achieve better performance in predicting RUL in roller bearings. Finally, the review finds existing research gaps and provides recommendations for future improvements, aiming to guide future researchers toward more accurate and reliable RUL prediction models for bearings.References
R. Palaniappan, “Predictive manufacturing: State of the art, design standards, limitations, challenges, and future perspectives,” 2024, p. 020004. doi: 10.1063/5.0235999.
Y. Lei, N. Li, L. Guo, N. Li, T. Yan, and J. Lin, “Machinery health prognostics: A systematic review from data acquisition to RUL prediction,” Mech Syst Signal Process, vol. 104, pp. 799–834, 2018, doi: https://doi.org/10.1016/j.ymssp.2017.11.016.
I. Nejjar, F. Geissmann, M. Zhao, C. Taal, and O. Fink, “Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction,” Reliab Eng Syst Saf, vol. 242, p. 109718, 2024, doi: https://doi.org/10.1016/j.ress.2023.109718.
Z. Zhao, Bin Liang, X. Wang, and W. Lu, “Remaining useful life prediction of aircraft engine based on degradation pattern learning,” Reliab Eng Syst Saf, vol. 164, pp. 74–83, 2017, doi: https://doi.org/10.1016/j.ress.2017.02.007.
L. Guo, N. Li, F. Jia, Y. Lei, and J. Lin, “A recurrent neural network based health indicator for remaining useful life prediction of bearings,” Neurocomputing, vol. 240, pp. 98–109, 2017, doi: https://doi.org/10.1016/j.neucom.2017.02.045.
C. Ferreira and G. Gonçalves, “Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods,” J Manuf Syst, vol. 63, pp. 550–562, 2022, doi: https://doi.org/10.1016/j.jmsy.2022.05.010.
L. Zhang, Z. Mu, and C. Sun, “Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Exponential Model and Particle Filter,” IEEE Access, vol. 6, pp. 17729–17740, 2018, doi: 10.1109/ACCESS.2018.2816684.
Y. Fan, S. Nowaczyk, and T. Rögnvaldsson, “Transfer learning for remaining useful life prediction based on consensus self-organizing models,” Reliab Eng Syst Saf, vol. 203, p. 107098, Nov. 2020, doi: 10.1016/j.ress.2020.107098.
M. Yan, X. Wang, B. Wang, M. Chang, and I. Muhammad, “Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model,” ISA Trans, vol. 98, pp. 471–482, Mar. 2020, doi: 10.1016/j.isatra.2019.08.058.
M. Baptista, E. M. P. Henriques, I. P. de Medeiros, J. P. Malere, C. L. Nascimento, and H. Prendinger, “Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering,” Reliab Eng Syst Saf, vol. 184, pp. 228–239, Apr. 2019, doi: 10.1016/j.ress.2018.01.017.
Z. Chen, Y. Li, T. Xia, and E. Pan, “Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy,” Reliab Eng Syst Saf, vol. 184, pp. 123–136, 2019, doi: https://doi.org/10.1016/j.ress.2017.09.002.
M. Elmahallawy, T. Elfouly, A. Alouani, and A. M. Massoud, “A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction,” IEEE Access, vol. 10, pp. 119040–119070, 2022, doi: 10.1109/ACCESS.2022.3221137.
M. S. Rathore and S. P. Harsha, “Prognostic Analysis of High-Speed Cylindrical Roller Bearing Using Weibull Distribution and k -Nearest Neighbor,” J Nondestruct Eval Diagn Progn Eng Syst, vol. 5, no. 1, Feb. 2022, doi: 10.1115/1.4051314.
Y. Zhou, M. Huang, and M. Pecht, “Remaining useful life estimation of lithium-ion cells based on k-nearest neighbor regression with differential evolution optimization,” J Clean Prod, vol. 249, p. 119409, 2020, doi: https://doi.org/10.1016/j.jclepro.2019.119409.
R. and N. S. K. and N. N. M. and V. V. and N. F. G. Abdalla Muhamed Abdelhamed Bakhet and Palaniappan, “An Overview of Industry 5.0: Key Features, Advantages, Limitations and Future Directions,” in Tech Fusion in Business and Society : Harnessing Big Data, IoT, and Sustainability in Business: Volume 1, R. K. Hamdan, Ed., Cham: Springer Nature Switzerland, 2025, pp. 633–643. doi: 10.1007/978-3-031-84628-1_53.
S. Khan, T. Yairi, S. Tsutsumi, and S. Nakasuka, “A review of physics-based learning for system health management,” Annu Rev Control, vol. 57, p. 100932, 2024, doi: https://doi.org/10.1016/j.arcontrol.2024.100932.
R. Palaniappan, “Cyber-Physical Systems: Design Standards, Applications, Limitations, Challenges, and Future Perspectives,” 2025, pp. 17–30. doi: 10.1007/978-3-031-77078-4_2.
R. Huang, L. Xi, X. Li, C. Richard Liu, H. Qiu, and J. Lee, “Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods,” Mech Syst Signal Process, vol. 21, no. 1, pp. 193–207, Jan. 2007, doi: 10.1016/j.ymssp.2005.11.008.
S. L. Chen, M. Craig, R. Callan, H. Powrie, and R. Wood, “Use of Artificial Intelligence Methods for Advanced Bearing Health Diagnostics and Prognostics,” in 2008 IEEE Aerospace Conference, IEEE, Mar. 2008, pp. 1–9. doi: 10.1109/AERO.2008.4526604.
C. Castejón, O. Lara, and J. C. García-Prada, “Automated diagnosis of rolling bearings using MRA and neural networks,” Mech Syst Signal Process, vol. 24, no. 1, pp. 289–299, Jan. 2010, doi: 10.1016/j.ymssp.2009.06.004.
C. Sun, Z. Zhang, and Z. He, “Research on bearing life prediction based on support vector machine and its application,” J Phys Conf Ser, vol. 305, p. 012028, Jul. 2011, doi: 10.1088/1742-6596/305/1/012028.
H.-E. Kim, A. C. C. Tan, J. Mathew, and B.-K. Choi, “Bearing fault prognosis based on health state probability estimation,” Expert Syst Appl, vol. 39, no. 5, pp. 5200–5213, Apr. 2012, doi: 10.1016/j.eswa.2011.11.019.
Z. Tian, “An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring,” J Intell Manuf, vol. 23, no. 2, pp. 227–237, Apr. 2012, doi: 10.1007/s10845-009-0356-9.
K. Medjaher, D. A. Tobon-Mejia, and N. Zerhouni, “Remaining Useful Life Estimation of Critical Components With Application to Bearings,” IEEE Trans Reliab, vol. 61, no. 2, pp. 292–302, Jun. 2012, doi: 10.1109/TR.2012.2194175.
X. Chen, Z. Shen, Z. He, C. Sun, and Z. Liu, “Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine,” Proc Inst Mech Eng C J Mech Eng Sci, vol. 227, no. 12, pp. 2849–2860, Dec. 2013, doi: 10.1177/0954406212474395.
A. Soualhi, K. Medjaher, and N. Zerhouni, “Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression,” IEEE Trans Instrum Meas, vol. 64, no. 1, pp. 52–62, Jan. 2015, doi: 10.1109/TIM.2014.2330494.
J. Ben Ali, B. Chebel-Morello, L. Saidi, S. Malinowski, and F. Fnaiech, “Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network,” Mech Syst Signal Process, vol. 56–57, pp. 150–172, May 2015, doi: 10.1016/j.ymssp.2014.10.014.
P. Bangalore and L. B. Tjernberg, “An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings,” IEEE Trans Smart Grid, vol. 6, no. 2, pp. 980–987, Mar. 2015, doi: 10.1109/TSG.2014.2386305.
M. Elforjani, “Estimation of Remaining Useful Life of Slow Speed Bearings Using Acoustic Emission Signals,” J Nondestr Eval, vol. 35, no. 4, p. 62, Dec. 2016, doi: 10.1007/s10921-016-0378-0.
L. Ren, J. Cui, Y. Sun, and X. Cheng, “Multi-bearing remaining useful life collaborative prediction: A deep learning approach,” J Manuf Syst, vol. 43, pp. 248–256, Apr. 2017, doi: 10.1016/j.jmsy.2017.02.013.
J. Deutsch and D. He, “Using Deep Learning-Based Approach to Predict Remaining Useful Life of Rotating Components,” IEEE Trans Syst Man Cybern Syst, vol. 48, no. 1, pp. 11–20, Jan. 2018, doi: 10.1109/TSMC.2017.2697842.
L. Ren, Y. Sun, H. Wang, and L. Zhang, “Prediction of Bearing Remaining Useful Life With Deep Convolution Neural Network,” IEEE Access, vol. 6, pp. 13041–13049, 2018, doi: 10.1109/ACCESS.2018.2804930.
H. O. Omoregbee, M. U. Olanipekun, and A. B. Edward, “Non Coding of Big Dataset and the use of Neural Network Regression Artificial Intelligence Model in Azure for Predicting the Remaining Useful Life (RUL) of Bearing,” in 2019 IEEE AFRICON, IEEE, Sep. 2019, pp. 1–7. doi: 10.1109/AFRICON46755.2019.9133738.
C. Cheng et al., “A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings,” IEEE/ASME Transactions on Mechatronics, vol. 25, no. 3, pp. 1243–1254, Jun. 2020, doi: 10.1109/TMECH.2020.2971503.
B.-L. Lu, Z.-H. Liu, H.-L. Wei, L. Chen, H. Zhang, and X.-H. Li, “A Deep Adversarial Learning Prognostics Model for Remaining Useful Life Prediction of Rolling Bearing,” IEEE Transactions on Artificial Intelligence, vol. 2, no. 4, pp. 329–340, Aug. 2021, doi: 10.1109/TAI.2021.3097311.
H. Ding, L. Yang, Z. Cheng, and Z. Yang, “A remaining useful life prediction method for bearing based on deep neural networks,” Measurement, vol. 172, p. 108878, Feb. 2021, doi: 10.1016/j.measurement.2020.108878.
Q. Wang, S. Wang, B. Wei, W. Chen, and Y. Zhang, “Weighted K-NN Classification Method of Bearings Fault Diagnosis With Multi-Dimensional Sensitive Features,” IEEE Access, vol. 9, pp. 45428–45440, 2021, doi: 10.1109/ACCESS.2021.3066489.
J. A. de Leon, X. Batiller, K. C. Fernandez, M. M. Vaay, C. R. Renosa, and R. Concepcion, “Remaining Useful Life Estimation for Roller Bearings through a Takagi-Sugeno Fuzzy Logic Model with Convolutional Neural Networks Consequents,” in 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), IEEE, Dec. 2022, pp. 1–6. doi: 10.1109/HNICEM57413.2022.10109404.
H. S. Kumar and G. Upadhyaya, “Fault diagnosis of rolling element bearing using continuous wavelet transform and K- nearest neighbour,” Mater Today Proc, vol. 92, pp. 56–60, 2023, doi: https://doi.org/10.1016/j.matpr.2023.03.618.
M. Motahari-Nezhad and S. M. Jafari, “Comparison of MLP and RBF neural networks for bearing remaining useful life prediction based on acoustic emission,” Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, vol. 237, no. 1, pp. 129–148, Jan. 2023, doi: 10.1177/13506501221106556.
R. Palaniappan, “Comparative Analysis of Support Vector Machine, Random Forest and k-Nearest Neighbor Classifiers for Predicting Remaining Usage Life of Roller Bearings,” Informatica, vol. 48, no. 7, Apr. 2024, doi: 10.31449/inf.v48i7.5726.
L. Magadán, J. C. Granda, and F. J. Suárez, “Robust prediction of remaining useful lifetime of bearings using deep learning,” Eng Appl Artif Intell, vol. 130, p. 107690, Apr. 2024, doi: 10.1016/j.engappai.2023.107690.
Y. Ma, “Bearing Life Prediction Model for Electromechanical Equipment by Integrating Deep Neural Network and K-Nearest Neighbor Algorithm and Its Application,” Journal of Theoretical and Applied Mechanics, vol. 62, no. 4, pp. 721–735, Oct. 2024, doi: 10.15632/jtam-pl/194237.
U. Farooq, M. Ademola, and A. Shaalan, “Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems,” Electronics (Basel), vol. 13, no. 2, p. 438, Jan. 2024, doi: 10.3390/electronics13020438.
K. Bharatheedasan, T. Maity, L. A. Kumaraswamidhas, and M. Durairaj, “Enhanced fault diagnosis and remaining useful life prediction of rolling bearings using a hybrid multilayer perceptron and LSTM network model,” Alexandria Engineering Journal, vol. 115, pp. 355–369, Mar. 2025, doi: 10.1016/j.aej.2024.12.007.
V. I. Vlachou et al., “Intelligent Fault Diagnosis of Ball Bearing Induction Motors for Predictive Maintenance Industrial Applications,” Machines, vol. 13, no. 10, p. 902, Oct. 2025, doi: 10.3390/machines13100902.
Y. Jin, D. Liu, Y. Xiao, and L. Cui, “Dual-channel dynamic spline graph convolutional network for bearing remaining useful life prediction,” Reliab Eng Syst Saf, vol. 266, p. 111731, Feb. 2026, doi: 10.1016/j.ress.2025.111731.
DOI:
https://doi.org/10.31449/inf.v50i6.12280Downloads
Published
How to Cite
Issue
Section
License
Authors retain copyright in their work. By submitting to and publishing with Informatica, authors grant the publisher (Slovene Society Informatika) the non-exclusive right to publish, reproduce, and distribute the article and to identify itself as the original publisher.
All articles are published under the Creative Commons Attribution license CC BY 3.0. Under this license, others may share and adapt the work for any purpose, provided appropriate credit is given and changes (if any) are indicated.
Authors may deposit and share the submitted version, accepted manuscript, and published version, provided the original publication in Informatica is properly cited.
