Jebur, N., Soud, W. (2025). Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies. , 43(1), 25-41. doi: 10.30684/etj.2024.148997.1737
Nazik A. Jebur; Wafa A. Soud. "Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies". , 43, 1, 2025, 25-41. doi: 10.30684/etj.2024.148997.1737
Jebur, N., Soud, W. (2025). 'Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies', , 43(1), pp. 25-41. doi: 10.30684/etj.2024.148997.1737
Jebur, N., Soud, W. Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies. , 2025; 43(1): 25-41. doi: 10.30684/etj.2024.148997.1737

Recent advances in journal bearings: wear fault diagnostics, condition monitoring and fault diagnosis methodologies

Engineering and Technology Journal
Volume 43, Issue 1, January 2025, Pages 25-41    PDF (973.73 K)
Document Type: Review Paper
DOI: 10.30684/etj.2024.148997.1737
Authors
Nazik A. Jebur* ; Wafa A. Soud
Mechanical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
Abstract
This review comprehensively encompasses a range of recent studies on journal bearings, emphasizing wear fault diagnostics, condition monitoring, and fault diagnosis methodologies. A significant finding reveals a shift back to the utilization of journal bearings in various rotating machinery such as compressors, motors, turbines, and pumps. Various methodologies employed in these recent studies include vibration analysis, machine learning, deep learning, and both numerical and experimental simulations. Key findings indicate that ensemble models, such as the CNN and deep neural network (CNNEPDNN) model, significantly improve convergence speed, test accuracy, and F-Score in bearing fault diagnosis by 15-20% compared to individual models. Additionally, convolutional autoencoders have demonstrated impressive performance, achieving an average Pearson coefficient of 91% in wear estimation, underscoring the critical importance of predictive maintenance. Despite these remarkable advancements, challenges persist due to the lack of uniform evaluation criteria and the focus on specific error types under particular operating conditions. Collaborative efforts among researchers are essential for developing robust and broadly applicable diagnostic models. Addressing these ongoing issues will further enhance condition monitoring and defect detection, leading to more reliable and academically rigorous diagnostic methods applicable in diverse real-world scenarios.

Highlights
  • Journal bearings have a resurgence in usage across compressors, motors, turbines, and pumps.
  • Advanced diagnostics integrate vibration analysis, machine learning, and simulations.
  • Ensemble models, like CNNEPDNN, enhance diagnostic metrics by 15-20%.
  • Convolutional autoencoders achieve 91% accuracy in wear estimation.
  • Challenges include uniform evaluation criteria and comprehensive diagnostic models.
Keywords
Journal bearings; Fault diagnostics; Vibration analysis; Deep learning; Bearing wear
References
  1. P. Raharjo, S. Abdusslam, F. Gu, A. Ball, Vibro-Acoustic characteristic of a self aligning spherical journal bearing due to eccentric bore fault,‏ Conf. Mach. Failure Prevention Technol.,
  2. O. Gecgel, J. Dias, S. Ekwaro-Osire, Alves, T. Machado, G. Daniel, K. Cavalca, Simulation-driven deep learning approach for wear diagnostics in hydrodynamic journal bearings, J. Tribol., 143 (2021) 084501.‏ https://doi.org/10.1115/1.4049067
  3. J. Gómez, F. Hernández Montero, J. Gómez Mancilla, Variable Selection for Journal Bearing Faults Diagnostic Through Logical Combinatorial Pattern Recognition: 6th International Workshop, IWAIPR 2018, Havana, Cuba, September 24–26, 2018, Proc., 11047 , 2018, 299-306. https://doi.org/10.1007/978-3-030-01132-1_34
  4. Bai, Y. Cheng, W. Wen, W. Liu, Y. Application of time-frequency analysis in rotating machinery fault diagnosis, Shock and Vibration, 2023. ‏https://doi.org/10.1155/2023/9878228
  5. C. Liu, F. Dong, K. Ge, Y. Tian, A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching, IEEE Photonics J., 16 (2024) 8600317. https://doi.org/10.1109/JPHOT.2024.3392392
  6. C. Liu, F. Dong, A New Framework Based on Supervised Joint Distribution Adaptation for Bearing Fault Diagnosis across Diverse Working Conditions,  Shock and Vibration, 2024 (2024) 8296809. https://doi.org/10.1155/2024/8296809
  7. Y. Li, A Review of Wind Turbine Bearing Fault Diagnosis, World Sci. Res. J., 10 (2024) 1-9. https://doi.org/10.6911/WSRJ.202402_10(2).0001
  8. A. Dubaish, A. Jaber, State-of-the-art review into signal processing and artificial intelligence-based approaches applied in gearbox defect diagnosis, Eng. Technol. J., 42 (2023) 157-172. http://dx.doi.org/10.30684/etj.2023.142462.1535
  9. G. Geetha, P. Geethanjali, An efficient method for bearing fault diagnosis, Syst. Sci. Control. Eng., 12 (2024) 2329264. https://doi.org/10.1080/21642583.2024.2329264
  10. J. Dai, L. Tian, H. Chang, An Intelligent Diagnostic Method for Wear Depth of Sliding Bearings Based on MGCNN, Machines, 12 (2024) 266. https://doi.org/10.3390/machines12040266
  11. Y. Liu, X. Xin,Y. Zhao, S. Ming, Y. Ma, J. Han, Study on coupling fault dynamics of sliding bearing-rotor system, J. Comput. Nonlinear Dynam., 14 (2019) 041005. https://doi.org/10.1115/1.4042688
  12. D. Liu, L. Cui , H. Wang, Rotating machinery fault diagnosis under time-varying speeds: A review, IEEE J. Sens., 23,2023, 29969-29990. https://doi.org/10.1109/JSEN.2023.3326112
  13. B.A. Tama, M. Vania, S. Lee, S. Lim‏ , Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals, Artificial Intelligence Review, 56 (2023) 4667-4709. https://doi.org/10.1007/s10462-022-10293-3
  14. H. Peng, H. Zhang, L. Shangguan, Y. Fan, Review of tribological failure analysis and lubrication technology research of wind power bearings, Polymers, 14 (2022) 3041. ‏ https://doi.org/10.3390/polym14153041
  15. M. Maurya, I. Panigrahi, D. Dash, C. Malla, Intelligent fault diagnostic system for rotating machinery based on IoT with cloud computing and artificial intelligence techniques: a review, Soft Comput., 28 (2023) 477– 494. http://dx.doi.org/10.1007/s00500-023-08255-0
  16. A. Moosavian, H. Ahmadi, A. Tabatabaeefar, B. Sakhaei, An appropriate procedure for detection of journal-bearing fault using power spectral density, k-nearest neighbor and support vector machine, Int. J. Smart. Sens. Intell. Syst., 5 (2012) 685-700.‏ https://doi.org/10.21307/ijssis-2017-502
  17. N. Thamba, H. Himamshu, P. Nayak, N. Chiluar, Journal bearing fault detection based on Daubechies wavelet, Arch. Acoust., 42 (2017) 401- 414. ‏ http://dx.doi.org/10.1515/aoa-2017-0042
  18. A. Kumar, P. Sathujoda, V. Ranjan,Vibration characteristics of a rotor-bearing system caused due to coupling misalignment–a review, Vib. Proced., 39 (2021) 1-10.‏ http://dx.doi.org/10.21595/vp.2021.22195
  19. Y. Wei, Y. Li, M. Xu, W. Huang, A review of early fault diagnosis approaches and their applications in rotating machinery, Entropy, 21 (2019) 409. http://dx.doi.org/10.3390/e21040409
  20. M. Romanssini, P. de Aguirre, L. Compassi-Severo, A. Girardi, A Review on Vibration Monitoring Techniques for Predictive Maintenance of Rotating Machinery, Eng., 4 (2023) 1797-1817.‏ https://doi.org/10.3390/eng4030102
  21. O. Das, D. Das, D. Birant, Machine learning for fault analysis in rotating machinery: A comprehensive review, Heliyon, 9 (2023) e17584. ‏ http://dx.doi.org/10.1016/j.heliyon.2023.e17584
  22. A. Nath, S. Udmale, S. Singh, Role of artificial intelligence in rotor fault diagnosis: A comprehensive review, Artif .Intell. Rev., 54 (2021) 2609-2668. ‏https://doi.org/10.1007/s10462-020-09910-w
  23. Ali, Y. 2018. Artificial intelligence application in machine condition monitoring and fault diagnosis, Artificial Intelligence: Emerging Trends and Applications, pp. 223–258. https://doi.org/10.5772/intechopen.74932
  24. R. Liu, B. Yang, E. Zio, X. Chen, Artificial intelligence for fault diagnosis of rotating machinery: A review, Mech. Syst. Signal Process., 108 (2018) 33-47.‏ https://doi.org/10.1016/j.ymssp.2018.02.016
  25. A. Ihsan, A .Wafa, Vibration Feature Extraction and Artificial Neural Network-based Approach for Balancing a Multi-disc Rotor System, J. Mech. Ind. Eng., 17 (2023) 429– 440. http://dx.doi.org/10.59038/jjmie/170312
  26. A. Baqer, A. Jaber, W. Soud, Prediction of the belt drive contamination status based on vibration analysis and artificial neural network, J. Intell. Fuzzy Syst., 45 (2023) 6629-6643. http://dx.doi.org/10.3233/JIFS-222438
  27. A. Sio-Iong, L. Gelman, H. Karimi, M. Tiboni, Advances in Machine Learning for Sensing and Condition Monitoring, Appl. Sci., 12 (2022) 12392.‏ https://doi.org/10.3390/app122312392
  28. G. Ciaburro, Machine fault detection methods based on machine learning algorithms: A review, Math. Biosci. Eng., 19 (2022) 11453-11490.‏ https://doi.org/10.3934/mbe.2022534
  29. G. Ciaburro, G. Iannace, Machine-learning-based methods for acoustic emission testing: A review, Appl. Sci., 12 (2022) 10476.‏ https://doi.org/10.3390/app122010476
  30. S. Sayyad, S. Kumar, A. Bongale, A. Bongale, S. Patil, Estimating remaining useful life in machines using artificial intelligence: A scoping review, Libr. Philos. Pract., (2021) 1-26.‏
  31. Z. Zhu, Y. Lei, G. Qi, Chai, N. Mazur, Y. An, X. Huang, A review of the application of deep learning in intelligent fault diagnosis of rotating machinery, Measurement, 206 (2023) 112346.‏ https://doi.org/10.1016/j.measurement.2022.112346
  32. D. Alves, T. Machado, K. Cavalca, O. Gecgel, J. Dias, S. Ekwaro-Osire, Simulation-Driven Deep Learning Approach for Condition Monitoring of Hydrodynamic Journal Bearings, J. Tribol., 143 (2019) http://dx.doi.org/10.1115/1.4049067
  33. J. Bote-Garcia, N . Mokthari, Gühmann, Wear monitoring of journal bearings with acoustic emission under different operating conditions, PHM Soc. European Conf., 5 , 2020, http://dx.doi.org/10.36001/phme.2020.v5i1.1202
  34. B. Wan, J. Yang, S. Sun, A Method for Monitoring Lubrication Conditions of Journal Bearings in a Diesel Engine Based on Contact Potential, Appl. Sci., 10 (2020) 5199.‏ https://doi.org/10.3390/app10155199
  35. K. Brethee, J. Ma, G. Ibrahim, F. Gu, A. Ball, Vibration Analysis for Diagnosis of Tribo-Dynamic Interaction in Journal Bearings, In International conference on the Efficiency and Performance Engineering Network, Mech. Mach. Sci., 129 (2022) 877-888.‏ https://doi.org/10.1007/978-3-031-26193-0_77
  36. S . Poddar, N. Tandon, Detection of particle contamination in journal bearing using acoustic emission and vibration monitoring techniques, Tribol. Int., 134 (2019) 154-164.‏ https://doi.org/10.1016/j.triboint.2019.01.050
  37. H. Li, J. Huang, S. Ji, Bearing fault diagnosis with a feature fusion method based on an ensemble convolutional neural network and deep neural network, Sensors, 19 (2019) 2034.‏ https://doi.org/10.3390/s19092034
  38. R. Ranjan, S. Ghosh, M. Kumar, Fault diagnosis of journal bearing in a hydropower plant using wear debris, vibration and temperature analysis: A case study, Proc. Inst. Mech. Eng. Part E, J. Proc. Mech. Eng., 234 (2020) 235-242.‏ https://doi.org/10.1177/0954408920910290
  39. J. Ma, H. Zhang, S. Lou, F. Chu, Z. Shi, F. Gu, A. Ball, Analytical and experimental investigation of vibration characteristics induced by tribofilm-asperity interactions in hydrodynamic journal bearings, Mech. Syst. Signal Process., 150 (2021)107227.‏ https://doi.org/10.1016/j.ymssp.2020.107227
  40. P. Hiralal, P. Dilip, Diagnosis of localized defects in floating bush bearings through time-frequency domain analysis, Maintenance, Reliability and Condition Monitoring, 3 (2023). http://dx.doi.org/10.21595/marc.2023.23699
  41. M. Siddiqui, A. Chodvadiya, J. Luo,The influence of journal bearings on the gearbox dynamics of a 5 MW wind turbine drivetrain, J. Phys. Conf. Ser., 2626 (2023) 012009. https://doi.org/10.1088/1742-6596/2626/1/012009
  42. H. Yi, H. Jung, Kim, K. Ryu, Static load characteristics of hydrostatic journal bearings: measurements and predictions, Sensors, 2 2(2022) 7466. https://doi.org/10.3390/s22197466
  43. D. Alves, G. Daniel, H. de Castro, T. Machado, K. Cavalca, O. Gecgel, S. Ekwaro-Osire, Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault, Mech. Mach. Theory., 149 (2020) 103835.‏ https://doi.org/10.1016/j.mechmachtheory.2020.103835
  44. J. Ma, C. Fu, W. Zhu, K. Lu, Y. Yang, Stochastic analysis of lubrication in misaligned journal bearings, J. Tribol., 144 (2022) 081802.‏ https://doi.org/10.1115/1.4053626
  45. C. Ates, T. Höfchen, M. Witt, R. Koch, Jörg Bauer, Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders, Sensors, 23 (2023) 9212.‏ https://doi.org/10.3390/s23229212
  46. L. Shi, S. Su, W. Wang, Gao, C. Chu, Bearing Fault Diagnosis Method Based on Deep Learning and Health State Division, Appl. Sci., 13 (2023) 7424.‏ https://doi.org/10.3390/app13137424
  47. B. Lehmann, P. Trompetter, F. Guzmán, G. Jacobs, Evaluation of Wear Models for the Wear Calculation of Journal Bearings for Planetary Gears in Wind Turbines, Lubricants, 11 (2023) 364.‏ https://doi.org/10.3390/lubricants11090364
  48. P. Li, H. Zhang, X. Li, Z. Shi, S. Xiao, F. Gu, Manufacturing error and misalignment effect on the transient lubrication behavior of dynamically loaded journal bearing with micro-groove, Phys. Fluids, 35 (2023) 073601. https://doi.org/10.1063/5.0157769
  49. H. Jamali, H. Sultan, A. Senatore, Z. Al-Dujaili, M. Jweeg, A. Abed, O. Abdullah, Minimizing Misalignment Effects in Finite Length Journal Bearings, Designs, 6 (2022) 85.‏ https://doi.org/10.3390/designs6050085
  50. H. Guo, J. Bao, S .Zhang, M. Shi, Experimental and Numerical Study on Mixed Lubrication Performance of Journal Bearing Considering Misalignment and Thermal Effect, Lubricants, 10 (2022) 262.‏ https://doi.org/10.3390/lubricants10100262
  51. H. Sayed, T. El-Sayed, M. Friswell, Continuation Analysis of Rotor Bearing Systems Through Direct Solution of Reynolds Equation, In Advances in Machinery, Materials Science and Engineering Application IX: Proceedings of the 9th International Conference MMSE, 40, 2023, 217. http://dx.doi.org/10.3233/ATDE230462
  52. B. Qian, Y. Ran, Ding, W. Sun, C. Ma, Experiment and Simulation Analysis of the Vibration Response of the Rotor-bearing System, Research Square, (2023) 1-24. ‏ https://doi.org/10.21203/rs.3.rs-1951821/v1
  53. M. Lucassen, T. Decker, F. Guzmán, B. Lehmann, D. Bosse, G. Jacobs, Simulation methodology for the identification of critical operating conditions of planetary journal bearings in wind turbines, Forsch . Ingenieurwes., 87 (2023) 147-157.‏ https://doi.org/10.1007/s10010-023-00626-1
  54. B. Lehmann, P. Trompetter, F. Guzmán, G. Jacobs, Evaluation of Wear Models for the Wear Calculation of Journal Bearings for Planetary Gears in Wind Turbines, Lubricants, 11 (2023) 364.‏ https://doi.org/10.3390/lubricants11090364
  55. A. Hamzah, A. Abbas, M. Mohammed, H. Aljibori, H. Jamali, O. Abdullah, An Evaluation of the Design Parameters of a Variable Bearing Profile Considering Journal Perturbation in Rotor–Bearing Systems, Designs, 7 (2023) 116.‏ https://doi.org/10.3390/designs7050116
  56. M. Altaf, T. Akram, M. Khan, M. Iqbal, M. Ch, C. Hsu, A new statistical features based approach for bearing fault diagnosis using vibration signals, Sensors, 22 (2022) 2012.‏ https://doi.org/10.3390/s22052012
  57. A. Bankova, Investigation of the Qualitative Dependence between the Character of Wear and the Mutual Location of Wearing Supports, In 2022 International Conference on Communications, Information, Electr. Energy Syst.,‏ 2022, 24-26. https://doi.org/10.1109/CIEES55704.2022.9990870
  58. T. Babu, A. Aravind, A. Rakesh, Jahzan, D . Prabha, M. Viswanathan, Automatic fault classification for journal bearings using ANN and DNN, Arch. Acoust., 43 (2018) 727–738. http://dx.doi.org/10.24425/aoa.2018.125166  
  59. S. Shakir, A. Jaber, Innovative Application of Artificial Neural Networks for Effective Rotational Shaft Crack Localization,‏ Innovative Corrosion Solutions, 52 (2024) 103-114. http://dx.doi.org/10.5937/fme2401103S
  60. D. S. Alves, T. Machado, K .L. Cavalca, O. Gecgel,. A Simulation-Driven Deep Learning Approach for Condition Monitoring of Hydrodynamic Journal Bearings. Part I: Diagnostics of Wear Faults,‏ Mech. Eng. Congress., 2019. http://dx.doi.org/10.26678/ABCM.COBEM2019.COB2019-0707
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