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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Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
A Critical Analysis of Crack Propagation Laws
Paris, P. C., & Erdoğan, F. (1963) · Journal of Basic Engineering · doi:10.1115/1.3656900 · Cited by 7,168
Bayes, T. (1763) · Philosophical Transactions of the Royal Society of London · doi:10.1098/rstl.1763.0053 · Cited by 3,023
AN ESSAY TOWARDS SOLVING A PROBLEM IN THE DOCTRINE OF CHANCES
BAYES (1958) · Biometrika · doi:10.1093/biomet/45.3-4.296 · Cited by 2,939
Intelligent Fault Diagnosis and Prognosis for Engineering Systems
Vachtsevanos, G., Lewis, F. L., Roemer, M. J., Hess, A. J., & Wu, B. (2006) · Wiley eBooks · doi:10.1002/9780470117842 · Cited by 1,134
Prognostics in Battery Health Management
Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J. (2008) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2008.4579269 · Cited by 594
A particle-filtering approach for on-line fault diagnosis and failure prognosis
Orchard, M. E., & Vachtsevanos, G. (2009) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331208092026 · Cited by 464
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
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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