- 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.,
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
|