Fault detection and diagnosis in process data using one-class support vector machines
By Sankar Mahadevan, Sirish L. Shah. 2009
367 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Control and Systems Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/5ef59a86bbea3d1a4a44530bc214262e80d95e3f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Analytical Chemistry
In Control and Systems Engineering
- Edwin David Lughofer
- Hajrudin Efendic
- Markus Pichler
- Kurt Pichler
- Francisco Serdio
- Thomas Buchegger
- S. Cheon
- Chang Ouk Kim
In Mechanical Engineering
- Edwin David Lughofer
- Hajrudin Efendic
- Markus Pichler
- Kurt Pichler
- Francisco Serdio
- Thomas Buchegger
- S. Cheon
- Chang Ouk Kim
Citations in the corpus
- Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations (2014, methodology): “The comparison of results will also include four renowned fault detection methods from literature: PCA-based with Q and Hotelling statistics [28], classical ARMA [8], one-class SVMs in the signal [29] and in the model space (combined with ARMA) [30].”
- A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes (2016, methodology): “Among them, Mahadevan and Shah [4] detected abnormal process behaviors in the semiconductor etching process using a one-class SVM.”