The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Data-driven monitoring for stochastic systems and its application on batch process
Abstract
Batch processes are characterised by a prescribed processing of raw materials into final products for a finite duration and play an important role in many industrial sectors due to the low-volume and high-value products. Process dynamics and stochastic disturbances are inherent characteristics of batch processes, which cause monitoring of batch processes a challenging problem in practice. To solve this problem, a subspace-aided data-driven approach is presented in this article for batch process monitoring. The advantages of the proposed approach lie in its simple form and its abilities to deal with stochastic disturbances and process dynamics existing in the process. The kernel density estimation, which serves as a non-parametric way of estimating the probability density function, is utilised for threshold calculation. An industrial benchmark of fed-batch penicillin production is finally utilised to verify the effectiveness of the proposed approach.
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Citations
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Cited by
Yin, S., Ding, S. X., Haghani, A., Hao, H., & Zhang, P. (2012) · Journal of Process Control · doi:10.1016/j.jprocont.2012.06.009 · Cited by 1,294
Self-citationData-based techniques focused on modern industry: An overview
Yin, S., Li, X., Gao, H., & Kaynak, O. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2308133 · Cited by 1,025
Self-citationBig Data for Modern Industry: Challenges and Trends [Point of View]
Yin, S., & Kaynak, O. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2388958 · Cited by 565
Self-citationData-driven modeling and learning in science and engineering
Montáns, F. J., Chinesta, F., Gómez-Bombarelli, R., & Kutz, J. N. (2019) · Comptes Rendus. Mécanique · doi:10.1016/j.crme.2019.11.009 · Cited by 299
Adaptive Neural Control of MIMO Nonstrict-Feedback Nonlinear Systems With Time Delay
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Metadata
- Authors
- Yin, S., Ding, S. X., Abandan Sari, A. H., & Hao, H.
- Venue
- International Journal of Systems Science
- Publisher
- Informa UK Limited
- Published
- 2013-07
- Volume
- 44
- Issue
- 7
- Pages
- 1366-1376
- ISSN
- 0020-7721 (ISSN-L)
- 1464-5319
- OpenAlex
- W1992742133
- Cited by
- 266
- References
- 32
- FWCI
- 90.41
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yin, S., Ding, S. X., Abandan Sari, A. H., & Hao, H. (2013). Data-driven monitoring for stochastic systems and its application on batch process. International Journal of Systems Science. https://doi.org/10.1080/00207721.2012.659708
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