The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis
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Citations
Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
System Identification: Theory for the User
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A Rationale and Test for the Number of Factors in Factor Analysis
Horn, J. L. (1965) · Psychometrika · doi:10.1007/bf02289447 · Cited by 8,714
Partial least-squares regression: a tutorial
Geladi, P., & Kowalski, B. R. (1986) · Analytica Chimica Acta · doi:10.1016/0003-2670(86)80028-9 · Cited by 6,992
Pattern classification and scene analysis
Chien, Y. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100577 · Cited by 3,398
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 User's Guide to Principal Components
Leigh, S., & Jackson, J. E. (1993) · Technometrics · doi:10.2307/1269292 · Cited by 3,332
Comparison of five rules for determining the number of components to retain.
Zwick, W. R., & Velicer, W. F. (1986) · Psychological Bulletin · doi:10.1037//0033-2909.99.3.432 · Cited by 3,129
Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
Wold, S. (1978) · Technometrics · doi:10.1080/00401706.1978.10489693 · Cited by 2,601
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
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017) · IEEE Access · doi:10.1109/access.2017.2756872 · Cited by 922
“the root causes of the fault may be located in a small part of the process or precisely to some specific sensors/actuators [26].”
BackgroundDeep convolutional neural network model based chemical process fault diagnosis
Wu, H., & Zhao, J. (2018) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2018.04.009 · Cited by 517
Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models
Yu, J., & Qin, S. J. (2008) · AIChE Journal · doi:10.1002/aic.11515 · Cited by 494
Advances and opportunities in machine learning for process data analytics
Qin, S. J., & Chiang, L. H. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.04.003 · Cited by 351
Self-citation
Metadata
- Authors
- Chiang, L. H., Russell, E. L., & Braatz, R. D.
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2000-03
- Volume
- 50
- Issue
- 2
- Pages
- 243-252
- ISSN
- 0169-7439 (ISSN-L)
- OpenAlex
- W1979357005
- Cited by
- 630
- References
- 28
- FWCI
- 10.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. https://app.cochat.ai/papers/10.1016/s0169-7439(99)00061-1
- The paper
- Chiang, L. H., Russell, E. L., & Braatz, R. D. (2000). Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/s0169-7439(99)00061-1
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.
