Comparative Analysis of Support Vector Machine, Random Forest and k-Nearest Neighbor Classifiers for Predicting Remaining Usage Life of Roller Bearings

Rajkumar Palaniappan

Abstract


Abstract: This research article aims to predict the remaining usage time of roller bearings using machine learning algorithms. The specific classifiers employed in this study are Support Vector Machines, Random Forest Classifier, and k-Nearest Neighbors. The predictive model takes into account various features including temperature, speed, load, dimensions of the inner and outer rings, width, vibration amplitude, vibration frequency, lubricant type, and lubricant viscosity. Data for training and testing the model were collected using a custom-made single bearing test rig. The target output variables are divided into intervals representing different percentages of remaining usage time. Principal component analysis (PCA) is utilized to identify the most influential features from the data. A ten-fold cross-validation method is employed for training and testing the classifiers. The features extracted through PCA are then fed into the classification model. The results show that the Support Vector Machines achieve the highest mean classification accuracy of 96.74%, followed by the Random Forest Classifier with 95.95%, and the k-Nearest Neighbors classifier with 91.77%. The study concludes that the Support Vector Machines outperform the Random Forest Classifier and k-Nearest Neighbors. Future research directions include exploring the application of deep learning algorithms to further enhance the predictive accuracy of the model. Additionally, conducting experiments with a larger and more diverse dataset, encompassing various operating conditions and types of bearings, would provide a broader understanding of the model's performance and generalizability.


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References


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.

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.

X. Li, F. Elasha, S. Shanbr, and D. Mba, “Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning,” Energies (Basel), vol. 12, no. 14, p. 2705, Jul. 2019, doi: 10.3390/en12142705.

Z. Esfahani, K. Salahshoor, B. Farsi, and U. Eicker, “A New Hybrid Model for RUL Prediction through Machine Learning,” Journal of Failure Analysis and Prevention, vol. 21, no. 5, pp. 1596–1604, Oct. 2021, doi: 10.1007/s11668-021-01205-8.

Z. Kang, C. Catal, and B. Tekinerdogan, “Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks,” Sensors, vol. 21, no. 3, p. 932, Jan. 2021, doi: 10.3390/s21030932.




DOI: https://doi.org/10.31449/inf.v48i7.5726

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