The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Fault detection and diagnosis in process data using one-class support vector machines
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Citations
Cites
1 retracted
Zhang, Y. (2008) · Industrial & Engineering Chemistry Research · doi:10.1021/ie071496x · Cited by 96
RetractedLIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,429
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
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
Estimation of Dependences Based on Empirical Data
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Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
Keerthi, S. S., & Lin, C.-J. (2003) · Neural Computation · doi:10.1162/089976603321891855 · Cited by 1,622
Statistical process monitoring: basics and beyond
Joe Qin, S. (2003) · Journal of Chemometrics · doi:10.1002/cem.800 · Cited by 1,582
Cited by
Deep 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
Lee, K. B., Cheon, S., & Kim, C. O. (2017) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2017.2676245 · Cited by 483
A deep belief network based fault diagnosis model for complex chemical processes
Zhang, Z., & Zhao, J. (2017) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2017.02.041 · Cited by 309
Model-based fault diagnosis for aerospace systems: a survey
Marzat, J., Piet-Lahanier, H., Damongeot, F., & Walter, E. (2012) · Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering · doi:10.1177/0954410011421717 · Cited by 278
Metadata
- Authors
- Mahadevan, S., & Shah, S. L.
- Venue
- Journal of Process Control
- Publisher
- Elsevier BV
- Published
- 2009-12
- Volume
- 19
- Issue
- 10
- Pages
- 1627-1639
- ISSN
- 0959-1524 (ISSN-L)
- OpenAlex
- W1965833531
- Cited by
- 368
- References
- 26
- FWCI
- 18.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mahadevan, S., & Shah, S. L. (2009). Fault detection and diagnosis in process data using one-class support vector machines. Journal of Process Control. https://doi.org/10.1016/j.jprocont.2009.07.011
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.
