Predicting Remaining Useful Life During the Healthy Stage in Rolling Bearings
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Published
May 30, 2025
Sebastián Echeverri Restrepo
Sébastien Blachère
Simón Tamayo Giraldo
Daniel Pino Muñoz
Cees Taal
Abstract
Quantitative predictions of the time before the spall initiation phase (origination of the first spall) in pristine ball bearings running under an applied load is of great industrial relevance, especially for systems that require high running accuracy and/or high-speed performance. Currently there are no available methodologies to predict the remaining life until the first spalling event exclusively from vibration signals. We present an end-to-end approach, based on deep learning (one dimensional convolutional layers combined with long short-term memory units), that is able to quantify the time before the origination of the first spall in ball bearings, having as sole input vibration measurements. The method has been validated on a set of bearings -- run to failure on independent but identical test-rigs -- which had not been considered during training.
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Keywords
Deep Learning, Subsurface Fatigue, Remaining life, Bearings, Convolutional neural networks, Long short-term memory
References
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Cui, L., Wang, X., Xu, Y., Jiang, H., & Zhou, J. (2019, mar). A novel Switching Unscented Kalman Filter method for remaining useful life prediction of rolling bearing. Measurement, 135, 678–684. doi: 10.1016/j.measurement.2018.12.028
Echeverri Restrepo, S., Ooi, S. W., Yan, P., Andric, P., Vegter, R. H., & Lai, J. (2021, mar). Dark etching regions under rolling contact fatigue: a review. Materials Science and Technology, 37(4), 347–376. doi: 10.1080/02670836.2021.1916252
Fu, H., & Rivera-D´ıaz-del Castillo, P. E. (2018, aug). A unified theory for microstructural alterations in bearing steels under rolling contact fatigue. Acta Materialia, 155, 43–55. doi: 10.1016/j.actamat.2018.05.056
Gabelli, A., & Morales-Espejel, G. E. (2019, mar). A model for hybrid bearing life with surface and subsurface survival. Wear, 422-423(October 2018), 223–234. doi: 10.1016/j.wear.2019.01.050
Glorot, X., & Bengio, Y. (2010, jan). Understanding the difficulty of training deep feedforward neural networks. In Y.W. Teh & M. Titterington (Eds.), Proceedings of the thirteenth international conference on artificial intelligence and statistics (Vol. 9, pp. 249–256). Chia Laguna Resort, Sardinia, Italy: PMLR.
Habbouche, H., Benkedjouh, T., & Zerhouni, N. (2021). Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition. International Journal of Advanced Manufacturing Technology, 114(1-2), 145–157. doi: 10.1007/s00170-021-06814-z
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Hase, A. (2020, mar). Early Detection and Identification of Fatigue Damage in Thrust Ball Bearings by an Acoustic Emission Technique. Lubricants, 8(3), 37. doi: 10.3390/lubricants8030037
Hendriks, J., Dumond, P., & Knox, D. A. (2022). Towards better benchmarking using the CWRU bearing fault dataset. Mechanical Systems and Signal Processing, 169(November 2021), 108732. doi: 10.1016/j.ymssp.2021.108732
Jalalahmadi, B., & Sadeghi, F. (2010, apr). A Voronoi FE Fatigue Damage Model for Life Scatter in Rolling Contacts. Journal of Tribology, 132(2), 1–14. doi: 10.1115/1.4001012
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Lees, A.W., Quiney, Z., Ganji, A., & Murray, B. (2011). The use of acoustic emission for bearing condition monitoring. Journal of Physics: Conference Series, 305(1). doi: 10.1088/1742-6596/305/1/012074
Liefstingh, M., Taal, C., Echeverri Restrepo, S., & Azarfar, A. (2021, nov). On the interpretation of deep learning models in bearing vibration diagnostics. Annual Conference of the PHM Society, 13(1), 1–9. doi: 10.36001/phmconf.2021.v13i1.3047
Lourari, A. W., Benkedjouh, T., El Yousfi, B., & Soualhi, A. (2024, jan). ANFIS-based Framework for the Prediction of Bearing’s Remaining Useful Life. International Journal of Prognostics and Health Management, 15(1). doi: 10.36001/ijphm.2024.v15i1.3791
Lu, W., Wang, Y., Zhang, M., & Gu, J. (2024, jan). Physics guided neural network: Remaining useful life prediction of rolling bearings using long short-term memory network through dynamic weighting of degradation process. Engineering Applications of Artificial Intelligence, 127(PB), 107350. doi: 10.1016/j.engappai.2023.107350
Lundberg, G., & Palmgren, A. (1947). Dynamic capacity of rolling bearings. Acta Polytechnica - Mechanical Engineering Series, 1, 4–51.
Magadán, L., Granda, J., & Suárez, F. (2024, apr). Robust prediction of remaining useful lifetime of bearings using deep learning. Engineering Applications of Artificial Intelligence, 130(November 2023), 107690. doi: 10.1016/j.engappai.2023.107690
Mahdavi, H., Poulios, K., Kadin, Y., & Niordson, C. F. (2022, mar). On the effect of microplasticity on crack initiation from subsurface defects in rolling contact fatigue. International Journal of Fatigue, 106870. doi: 10.1016/j.ijfatigue.2022.106870
Medjaher, K., Tobon-Mejia, D. A., & Zerhouni, N. (2012, jun). Remaining Useful Life Estimation of Critical Components With Application to Bearings. IEEE Transactions on Reliability, 61(2), 292–302. doi: 10.1109/TR.2012.2194175
Ringsberg, J. (2001, aug). Life prediction of rolling contact fatigue crack initiation. International Journal of Fatigue, 23(7), 575–586. doi: 10.1016/S0142-1123(01)00024-X
Tobon-Mejia, D. A., Medjaher, K., Zerhouni, N., & Tripot, G. (2012, jun). A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models. IEEE Transactions on Reliability, 61(2), 491–503. doi: 10.1109/TR.2012.2194177
Voskamp, A. P. (1997). Microstructural changes during rolling rolling contact fatigue (Doctoral Thesis). Delft University of Technology.
Wan, G. T. Y., Amerongen, E. V., & Lankamp, H. (1992, jan). Effect of extreme-pressure additives on rolling bearing fatigue life. Journal of Physics D: Applied Physics, 25(1A), A147–A153. doi: 10.1088/0022-3727/25/1A/023
Warhadpande, A., Sadeghi, F., & Evans, R. D. (2013, may). Microstructural Alterations in Bearing Steels under Rolling Contact Fatigue Part 1—Historical Overview. Tribology Transactions, 56(3), 349–358. doi: 10.1080/10402004.2012.754073
Cheng,W., Cheng, H. S., Mura, T., & Keer, L. M. (1994). Micromechanics modeling of crack initiation under contact fatigue. Journal of Tribology, 116(1), 2–8. doi: 10.1115/1.2927042
Chollet, F., & Others. (2015). Keras. https://keras.io.
Cui, L., Wang, X., Xu, Y., Jiang, H., & Zhou, J. (2019, mar). A novel Switching Unscented Kalman Filter method for remaining useful life prediction of rolling bearing. Measurement, 135, 678–684. doi: 10.1016/j.measurement.2018.12.028
Echeverri Restrepo, S., Ooi, S. W., Yan, P., Andric, P., Vegter, R. H., & Lai, J. (2021, mar). Dark etching regions under rolling contact fatigue: a review. Materials Science and Technology, 37(4), 347–376. doi: 10.1080/02670836.2021.1916252
Fu, H., & Rivera-D´ıaz-del Castillo, P. E. (2018, aug). A unified theory for microstructural alterations in bearing steels under rolling contact fatigue. Acta Materialia, 155, 43–55. doi: 10.1016/j.actamat.2018.05.056
Gabelli, A., & Morales-Espejel, G. E. (2019, mar). A model for hybrid bearing life with surface and subsurface survival. Wear, 422-423(October 2018), 223–234. doi: 10.1016/j.wear.2019.01.050
Glorot, X., & Bengio, Y. (2010, jan). Understanding the difficulty of training deep feedforward neural networks. In Y.W. Teh & M. Titterington (Eds.), Proceedings of the thirteenth international conference on artificial intelligence and statistics (Vol. 9, pp. 249–256). Chia Laguna Resort, Sardinia, Italy: PMLR.
Habbouche, H., Benkedjouh, T., & Zerhouni, N. (2021). Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition. International Journal of Advanced Manufacturing Technology, 114(1-2), 145–157. doi: 10.1007/s00170-021-06814-z
Harris, T. A., & Kotzalas, M. N. (2006). Advanced Concepts of Bearing Technology: Rolling Bearing Analysis, Fifth Edition. CRC Press.
Hase, A. (2020, mar). Early Detection and Identification of Fatigue Damage in Thrust Ball Bearings by an Acoustic Emission Technique. Lubricants, 8(3), 37. doi: 10.3390/lubricants8030037
Hendriks, J., Dumond, P., & Knox, D. A. (2022). Towards better benchmarking using the CWRU bearing fault dataset. Mechanical Systems and Signal Processing, 169(November 2021), 108732. doi: 10.1016/j.ymssp.2021.108732
Jalalahmadi, B., & Sadeghi, F. (2010, apr). A Voronoi FE Fatigue Damage Model for Life Scatter in Rolling Contacts. Journal of Tribology, 132(2), 1–14. doi: 10.1115/1.4001012
Kingma, D. P., & Ba, J. (2014, dec). Adam: A Method for Stochastic Optimization. , 1–15.
Lees, A.W., Quiney, Z., Ganji, A., & Murray, B. (2011). The use of acoustic emission for bearing condition monitoring. Journal of Physics: Conference Series, 305(1). doi: 10.1088/1742-6596/305/1/012074
Liefstingh, M., Taal, C., Echeverri Restrepo, S., & Azarfar, A. (2021, nov). On the interpretation of deep learning models in bearing vibration diagnostics. Annual Conference of the PHM Society, 13(1), 1–9. doi: 10.36001/phmconf.2021.v13i1.3047
Lourari, A. W., Benkedjouh, T., El Yousfi, B., & Soualhi, A. (2024, jan). ANFIS-based Framework for the Prediction of Bearing’s Remaining Useful Life. International Journal of Prognostics and Health Management, 15(1). doi: 10.36001/ijphm.2024.v15i1.3791
Lu, W., Wang, Y., Zhang, M., & Gu, J. (2024, jan). Physics guided neural network: Remaining useful life prediction of rolling bearings using long short-term memory network through dynamic weighting of degradation process. Engineering Applications of Artificial Intelligence, 127(PB), 107350. doi: 10.1016/j.engappai.2023.107350
Lundberg, G., & Palmgren, A. (1947). Dynamic capacity of rolling bearings. Acta Polytechnica - Mechanical Engineering Series, 1, 4–51.
Magadán, L., Granda, J., & Suárez, F. (2024, apr). Robust prediction of remaining useful lifetime of bearings using deep learning. Engineering Applications of Artificial Intelligence, 130(November 2023), 107690. doi: 10.1016/j.engappai.2023.107690
Mahdavi, H., Poulios, K., Kadin, Y., & Niordson, C. F. (2022, mar). On the effect of microplasticity on crack initiation from subsurface defects in rolling contact fatigue. International Journal of Fatigue, 106870. doi: 10.1016/j.ijfatigue.2022.106870
Medjaher, K., Tobon-Mejia, D. A., & Zerhouni, N. (2012, jun). Remaining Useful Life Estimation of Critical Components With Application to Bearings. IEEE Transactions on Reliability, 61(2), 292–302. doi: 10.1109/TR.2012.2194175
Ringsberg, J. (2001, aug). Life prediction of rolling contact fatigue crack initiation. International Journal of Fatigue, 23(7), 575–586. doi: 10.1016/S0142-1123(01)00024-X
Tobon-Mejia, D. A., Medjaher, K., Zerhouni, N., & Tripot, G. (2012, jun). A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models. IEEE Transactions on Reliability, 61(2), 491–503. doi: 10.1109/TR.2012.2194177
Voskamp, A. P. (1997). Microstructural changes during rolling rolling contact fatigue (Doctoral Thesis). Delft University of Technology.
Wan, G. T. Y., Amerongen, E. V., & Lankamp, H. (1992, jan). Effect of extreme-pressure additives on rolling bearing fatigue life. Journal of Physics D: Applied Physics, 25(1A), A147–A153. doi: 10.1088/0022-3727/25/1A/023
Warhadpande, A., Sadeghi, F., & Evans, R. D. (2013, may). Microstructural Alterations in Bearing Steels under Rolling Contact Fatigue Part 1—Historical Overview. Tribology Transactions, 56(3), 349–358. doi: 10.1080/10402004.2012.754073
Section
Technical Papers