The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
Abstract
Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Fréchet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.
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Citations
Cites
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.016 · Cited by 2,634
Self-citationRemaining useful life estimation – A review on the statistical data driven approaches
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2010.11.018 · Cited by 2,162
A recurrent neural network based health indicator for remaining useful life prediction of bearings
Guo, L., Li, N., Jia, F., Lei, Y., & Lin, J. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.02.045 · Cited by 1,231
Self-citationHe, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105
PRONOSTIA : An experimental platform for bearings accelerated degradation tests.
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel‐Morello, B., Noureddine Zerhouni, A., & Varnier, C. (2012) · HAL · Cited by 928
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
Zhang, C., Lim, P., Qin, A. K., & Tan, K. C. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582798 · Cited by 870
Fast Marginal Likelihood Maximisation for Sparse Bayesian Models
Tipping, M. E., & Faul, A. (2003) · Cited by 775
A review on data-driven fault severity assessment in rolling bearings
Cerrada, M., Sánchez, R.-V., Li, C., Pacheco, F., Cabrera, D., Valente de Oliveira, J., & Vásquez, R. E. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.06.012 · Cited by 724
An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings
Li, N., Lei, Y., Lin, J., & Ding, S. X. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2455055 · Cited by 686
Self-citationBearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression
Soualhi, A., Medjaher, K., & Zerhouni, N. (2015) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2014.2330494 · Cited by 665
Cited by
Zhao, Z., Li, T., Wu, J., Sun, C., Wang, S., Yan, R., & Chen, X. (2020) · ISA Transactions · doi:10.1016/j.isatra.2020.08.010 · Cited by 688
Cao, Y., Ding, Y., Jia, M., & Tian, R. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107813 · Cited by 361
A Time Series Transformer based method for the rotating machinery fault diagnosis
Jin, Y., Hou, L., & Chen, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.111 · Cited by 336
An incorrect data detection method for big data cleaning of machinery condition monitoring
Xu, X., Lei, Y., & Li, Z. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2903774 · Cited by 134
Self-citationZhou, J., Qin, Y., Luo, J., & Zhu, T. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3218665 · Cited by 98
Metadata
- Authors
- Wang, B., Lei, Y., Li, N., & Li, N.
- Venue
- IEEE Transactions on Reliability
- Published
- 2020-03
- Volume
- 69
- Issue
- 1
- Pages
- 401-412
- ISSN
- 0018-9529 (ISSN-L)
- 1558-1721
- OpenAlex
- W2904460913
- Cited by
- 1,917
- References
- 35
- FWCI
- 44.06
- Type
- journal article
- Language
- en
- Open access
- closed
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