The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Review on data-driven modeling and monitoring for plant-wide industrial processes
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Citations
Cites
A plant-wide industrial process control problem
Downs, J. J., & Vogel, E. F. (1993) · Computers & Chemical Engineering · doi:10.1016/0098-1354(93)80018-i · Cited by 3,348
A review of process fault detection and diagnosis
Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., & Yin, K. (2003) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(02)00162-x · Cited by 1,859
Data-driven Soft Sensors in the process industry
Kadlec, P., Gabrys, B., & Strandt, S. (2009) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2008.12.012 · Cited by 1,845
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
Yin, S., Ding, S. X., Xie, X., & Luo, H. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2301773 · Cited by 1,716
Survey on data-driven industrial process monitoring and diagnosis
Qin, S. J. (2012) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2012.09.004 · Cited by 1,470
Review of Recent Research on Data-Based Process Monitoring
Ge, Z., Song, Z., & Gao, F. (2013) · Industrial & Engineering Chemistry Research · doi:10.1021/ie302069q · Cited by 967
Self-citationData-driven Design of Fault Diagnosis and Fault-tolerant Control Systems
Ding, S. X. (2014) · Advances in Industrial Control · doi:10.1007/978-1-4471-6410-4 · Cited by 669
Process data analytics in the era of big data
Qin, S. J. (2014) · AIChE Journal · doi:10.1002/aic.14523 · Cited by 383
Jiang, Q., Yan, X., & Huang, B. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2466557 · Cited by 361
Process Monitoring Approach Using Fast Moving Window PCA
Wang, X., Krüger, U., & Irwin, G. W. (2005) · Industrial & Engineering Chemistry Research · doi:10.1021/ie048873f · Cited by 323
Cited by
Zhu, J., Ge, Z., Song, Z., & Gao, F. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.09.003 · Cited by 308
Self-citationPerera, Y. S., Ratnaweera, D. A. A. C., Dasanayaka, C. H., & Abeykoon, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105988 · Cited by 286
Metadata
- Authors
- Ge, Z.
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 171
- Pages
- 16-25
- ISSN
- 0169-7439 (ISSN-L)
- OpenAlex
- W2757109865
- Cited by
- 630
- References
- 48
- FWCI
- 45.63
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Ge, Z. (2017). Review on data-driven modeling and monitoring for plant-wide industrial processes. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2017.09.021
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.
