Optimal scheduling of the maintenance of bearings in rotating machinery requires accurate remaining useful life (RUL) prediction during the entire lifetime of the bearing. For that reason, this paper proposes a sequential hybrid method that combines the strengths of statistical and data-driven approaches. A statistical model-based approach is preferred before a bearing fault is detected, and a data-driven approach once a bearing fault is detected from the vibration measurements. The method is tested and evaluated on an extensive dataset of accelerated lifetime tests of deep groove ball bearings. It is shown that the method, with a limited amount of training data, delivers accurate RUL predictions during both the healthy stage of the bearing lifetime, as well as during the final stages of increasing degradation under both constant and varying speed conditions.
How to Cite
autoencoder, hybrid RUL, bearing, remaining useful life
machinery. Journal of Manufacturing Systems, 56, 463-469.
Berghout, T., & Benbouzid, M. (2022). A systematic guide for predicting remaining useful life with machine learning. Electronics, 11(7).
Bourgana, T., Brijder, R., Ooijevaar, T., & Ompusunggu, A. (2021). Wavelet scattering network based bearing fault detection. PHM Society European Conference.
Brijder, R., Helsen, S., & Ompusunggu, A. (2023). Switching kalman filtering-based corrosion detection and prognostics for offshore wind-turbine structures. Wind, 3.
Dong, S., & Luo, T. (2013). Bearing degradation process prediction based on the pca and optimized ls-svm model. Measurement, 46(9), 3143-3152.
Ferreira, C., & Gonc¸alves, G. (2022). Remaining useful life prediction and challenges: A literature review on the use of machine learning methods. Journal of Manufacturing Systems, 63, 550-562.
Gebraeel, N. (2006). Sensory-updated residual life distributions for components with exponential degradation patterns.
IEEE Transactions on Automation Science and Engineering, 3(4), 382-393. Gebraeel, N., Lawley, M., Li, R., & Ryan, J. (2005, 06).
Residual-life distribution from component degradation signals: A bayesian approach. IIE Transactions, 37,
Halme, J., & Andersson, P. (2009). Rolling contact fatigue and wear fundamentals for rolling bearing diagnostics - state of the art. Journal of Engineering Tribology, 224, 377–393.
Harris, T. A., & Yu, W. K. (1999, 01). Lundberg-palmgren fatigue theory: Considerations of failure stress and stressed volume. Journal of Tribology, 121(1), 85-89. doi: 10.1115/1.2833815
Herve de Beaulieu, M., Shekhar Jha, M., Garnier, H., & Cerbah, F. (2022). Unsupervised prognostics based on
deep virtual health index prediction. In Proceedings of the european conference of the phm society 2022
(Vol. 7, p. 193–199).
ISO281. (2007). Rolling bearings – Dynamic load ratings and rating life (Standard). ISO: International Organization
Jammu, N., & Kankar, P. (2011, 10). A review on prognosis of rolling element bearings. International Journal of
Engineering Science and Technology, 3. Kamat, P., Sugandhi, R., & Kumar, S. (2021). Deep learningbased
anomaly-onset aware remaining useful life estimation of bearings. PeerJ Computer Science.
Kan, M. S., Tan, A. C., & Mathew, J. (2015). A review on prognostic techniques for non-stationary and nonlinear rotating systems. Mechanical Systems and Signal Processing, 62-63, 1-20.
Liao, L., & Kottig, F. (2014). Review of hybrid prognostics approaches for remaining useful life prediction of
engineered systems, and an application to battery life prediction. IEEE Transactions on Reliability, 63(1), 191-207.
Mrugalska, B. (2019). Remaining useful life as prognostic approach: A review. In (pp. 689–695). Springer International
NSWC, N. S. W. C. (2011). Handbook of reliability prediction procedures for mechanical equipment. West
Ooijevaar, T., Pichler, K., Di, Y., Devos, S., Volckaert, B., Van Hoecke, S., & Hesch, C. (2019). Smart machine maintenance enabled by a condition monitoring living lab. IFAC-PapersOnLine, 52(15), 376-381.
SKF. (2011). SKF bearing maintenance handbook. Wan, E., & Van Der Merwe, R. (2000). The unscented
kalman filter for nonlinear estimation. In Proceedings of the ieee 2000 adaptive systems for signal processing, communications, and control symposium (p. 153-158).
Zhao, S., Zhang, Y.,Wang, S., Zhou, B., & Cheng, C. (2019). A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method. Measurement, 146, 279-288.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.