The DOI matches its Crossref record; 152 of the 152 references it cites were found in a registry or an index.
Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods
Abstract
Abstract Degradation-based modeling methods have been recognized as an essential and effective approach for lifetime and remaining useful life (RUL) estimations for various health management activities that can be scheduled to ensure reliable, safe, and economical operation of deteriorating systems. As one of the most popular stochastic modeling methods, the previous several decades have witnessed remarkable developments and extensive applications of Wiener-process-based methods. However, there is no systematic review particularly focused on this topic. Therefore, this paper reviews recent modeling developments of the Wiener-process-based methods for degradation data analysis and RUL estimation, as well as their applications in the field of prognostics and health management (PHM). After a brief introduction of conventional Wiener-process-based degradation models, we pay particular attention to variants of the Wiener process by considering nonlinearity, multi-source variability, covariates, and multivariate involved in the degradation processes. In addition, we discuss the applications of the Wiener-process-based models for degradation test design and optimal decision-making activities such as inspection, condition-based maintenance (CBM), and replacement. Finally, we highlight several future challenges deserving further studies.
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Cites
An Analysis of Transformations
Box, G. E. P., & Cox, D. R. (1964) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1964.tb00553.x · Cited by 15,052
A review on machinery diagnostics and prognostics implementing condition-based maintenance
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2005.09.012 · Cited by 4,816
Remaining 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
Self-citationLee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.06.004 · Cited by 1,594
A survey of maintenance policies of deteriorating systems
Wang, H. (2002) · European Journal of Operational Research · doi:10.1016/s0377-2217(01)00197-7 · Cited by 1,592
A survey of the application of gamma processes in maintenance
van Noortwijk, J. M. (2009) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2007.03.019 · Cited by 1,359
Statistical Methods for Reliability Data
Nelson, W., Meeker, W. Q., & Escobar, L. A. (1998) · Technometrics · doi:10.2307/1271181 · Cited by 1,214
Prognostic modelling options for remaining useful life estimation by industry
Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2010.11.018 · Cited by 947
Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach
Ait-Sahalia, Y. (2002) · Econometrica · doi:10.1111/1468-0262.00274 · Cited by 902
A review on condition-based maintenance optimization models for stochastically deteriorating system
Alaswad, S., & Xiang, Y. (2017) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2016.08.009 · Cited by 796
Cited by
Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320
Wang, Z., Feng, G., Zhen, D., Gu, F., & Ball, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.113 · Cited by 441
Chen, J., Huang, R., Chen, Z., Mao, W., & Li, W. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110239 · Cited by 267
Zheng, X., Fan, W., Chen, C., & Peng, Z. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3359212 · Cited by 48
Lyu, D., Zhang, B., Liu, E., Yang, T., & Xiang, J. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2024.113713 · Cited by 25
Metadata
- Authors
- Zhang, Z., Si, X., Hu, C., & Lei, Y.
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 271
- Issue
- 3
- Pages
- 775-796
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W2791384746
- Cited by
- 696
- References
- 152
- FWCI
- 71.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, Z., Si, X., Hu, C., & Lei, Y. (2018). Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2018.02.033
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