The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab
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In the citing paper
“In Bayesian inference, the observations are used to estimate and update parameters with a form of a probability density function (PDF) [118].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [118]
In the citing paper
“When a new measurement is available, the posterior from the previous step is used as the prior information at the current step and the parameters are therefore updated by multiplying it with the likelihood [118].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [118]
Citations
Cites
A New Approach to Linear Filtering and Prediction Problems
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A particle-filtering approach for on-line fault diagnosis and failure prognosis
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Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life
Hu, C. K., Youn, B. D., Wang, P., & Yoon, J. T. (2012) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2012.03.008 · Cited by 388
Particle filtering prognostic estimation of the remaining useful life of nonlinear components
Zio, E., & Peloni, G. (2010) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2010.08.009 · Cited by 341
Zio, E., & Maio, F. D. (2009) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2009.08.001 · Cited by 335
On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
Pitt, M. K., Silva, R. D. S., Giordani, P. E., & Kohn, R. (2012) · Journal of Econometrics · doi:10.1016/j.jeconom.2012.06.004 · Cited by 266
Model-Based Prognostic Techniques Applied to a Suspension System
Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2008.2001055 · Cited by 234
Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life
Hu, C. K., Youn, B. D., & Wang, P. (2011) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2011.6024361 · Cited by 205
An engineering model of fatigue crack growth under variable amplitude loading
Huang, X., TORGEIR, M., & Cui, W. (2007) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2007.03.004 · Cited by 202
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Multiple damage progression paths in model-based prognostics
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Identification of correlated damage parameters under noise and bias using Bayesian inference
An, D., Choi, J.-H., & Kim, N. H. (2011) · Structural Health Monitoring · doi:10.1177/1475921711424520 · Cited by 59
Self-citationParameter estimation of railway vehicle dynamic model using rao-blackwellised particle filter
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A probabilistic-based airframe integrity management model
Wang, X., Rabiei, M., Hurtado, J., Modarres, M., & Hoffman, P. C. (2008) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2008.10.010 · Cited by 48
In situ monitoring and prediction of progressive joint wear using Bayesian statistics
An, D., Choi, J.-H., Schmitz, T. L., & Kim, N. H. (2011) · Wear · doi:10.1016/j.wear.2011.02.010 · Cited by 31
Self-citationExact Nonlinear Filtering and Prediction in Process Model-Based Prognostics
DeCastro, J., Tang, L. D., Loparo, K. A., Goebel, K., & Vachtsevanos, G. (2014) · Cited by 20
A Bayesian approach for the estimation of model parameters from noisy data sets
Payne, S. J. (2005) · IEEE Signal Processing Letters · doi:10.1109/lsp.2005.849542 · Cited by 8
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Cited by
Sorted by citations
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
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193
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 688
A review of the state of health for lithium-ion batteries: Research status and suggestions
Tian, H., Qin, P., Li, K., & Zhao, Z. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120813 · Cited by 675
A Model-Based Method for Remaining Useful Life Prediction of Machinery
Lei, Y., Li, N., Gontarz, S., Lin, J., Radkowski, S., & Dybała, J. (2016) · IEEE Transactions on Reliability · doi:10.1109/tr.2016.2570568 · Cited by 618
Metadata
- Authors
- An, D., Choi, J.-H., & Kim, N. H.
- Affiliations
- Korea Aerospace University (KR)
- University of Florida (US)
- Corresponding author
- Kim, Nam Ho
- Venue
- Reliability Engineering & System Safety
- Publisher
- Elsevier BV
- Published
- 2013-07
- Volume
- 115
- Pages
- 161-169
- ISSN
- 0951-8320 (ISSN-L)
- OpenAlex
- W2097956058
- Cited by
- 300
- References
- 22
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 16 · 2023: 29 · 2022: 31 · 2021: 40 · 2020: 25 · 2019: 37 · 2018: 33 · 2017: 30 · 2016: 17 · 2015: 15 · 2014: 9 · 2013: 2
- FWCI
- 40.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Reliability Engineering & System Safety (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- International Collaborative R&D Program (0420-2011-0161)
- Korea Institute of Energy Technology Evaluation and Planning (KETEP)
- Korean government's Ministry of Knowledge Economy
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Keywords
- model-based prognostics, particle filter, remaining useful life prediction, parameter estimation, degradation modeling, battery degradation, crack propagation, prediction intervals, damage evolution, MATLAB implementation
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