The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A data-model-fusion prognostic framework for dynamic system state forecasting
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Citations
Cites
Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches
Simon, D. (2006) · doi:10.1002/0470045345 · Cited by 4,875
Prognostics methods for battery health monitoring using a bayesian framework
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831
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
An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W. (2010) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2010.v2i1.1896 · Cited by 274
Self-citation
Cited by
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
Aboelmaged, M. G. (2014) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.05.002 · Cited by 355
Guha, A., & Patra, A. (2018) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2017.2776558 · Cited by 318
A novel health indicator for on-line lithium-ion batteries remaining useful life prediction
Zhou, Y., Huang, M., Chen, Y., & Tao, Y. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.04.119 · Cited by 289
Liao, L., & Köttig, F. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.03.013 · Cited by 258
Metadata
- Authors
- Liu, J., Wang, W., Ma, F., Yang, Y. B., & Yang, C. S.
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2012-06
- Volume
- 25
- Issue
- 4
- Pages
- 814-823
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W2020776245
- Cited by
- 190
- References
- 4
- FWCI
- 19.64
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A data-model-fusion prognostic framework for dynamic system state forecasting. https://app.cochat.ai/papers/10.1016/j.engappai.2012.02.015
- The paper
- Liu, J., Wang, W., Ma, F., Yang, Y. B., & Yang, C. S. (2012). A data-model-fusion prognostic framework for dynamic system state forecasting. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2012.02.015
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
