Fault Detection of Complex Processes Using nonlinear Mean Function Based Gaussian Process Regression: Application to the Tennessee Eastman Process
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-020-05052-x.pdf
Reference62 articles.
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4. Russell, E.L.; Chiang, L.H.; Braatz, R.D.: Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis. Chemom. Intell. Lab. Syst. 51, 81–93 (2000). https://doi.org/10.1016/S0169-7439(00)00058-7
5. Shao, J.D.; Rong, G.; Lee, J.M.: Learning a data-dependent kernel function for KPCA-based nonlinear process monitoring. Chem. Eng. Res. Des. 87, 1471–1480 (2009). https://doi.org/10.1016/j.cherd.2009.04.011
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