Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA. Fazai, R., Mansouri, M., Taouali, O., Harkat, M., Bouguila, N., & Nounou, M. In 2018 European Control Conference (ECC), pages 2697–2702, June, 2018. doi abstract bibtex In this paper, we propose an online reduced kernel principal component analysis (KPCA) method for process monitoring. The developed method consists in updating the KPCA model depending on the dictionary which contains linearly independent kernel functions and then using this new reduced KPCA model for process monitoring. The process monitoring performances are studied using Tennessee Eastman Process (TEP). The results demonstrate the effectiveness of the developed online KPCA technique compared to the classical online KPCA method.
@inproceedings{fazai_fault_2018,
title = {Fault {Detection} of the {Tennessee} {Eastman} {Process} using {Online} {Reduced} {Kernel} {PCA}},
doi = {10.23919/ECC.2018.8550213},
abstract = {In this paper, we propose an online reduced kernel principal component analysis (KPCA) method for process monitoring. The developed method consists in updating the KPCA model depending on the dictionary which contains linearly independent kernel functions and then using this new reduced KPCA model for process monitoring. The process monitoring performances are studied using Tennessee Eastman Process (TEP). The results demonstrate the effectiveness of the developed online KPCA technique compared to the classical online KPCA method.},
booktitle = {2018 {European} {Control} {Conference} ({ECC})},
author = {Fazai, Radhia and Mansouri, Majdi and Taouali, Okba and Harkat, Mohamed-Faouzi and Bouguila, Nassreddine and Nounou, Mohamed},
month = jun,
year = {2018},
keywords = {Dictionaries, Erbium, Indexes, Inductors, Kernel, Monitoring, Principal component analysis},
pages = {2697--2702},
}
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