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Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels

Autor
Askarian, M.; Benitez, R.; Graells, M.; Zarghami, R.
Tipus d'activitat
Article en revista
Revista
Expert systems with applications
Data de publicació
2016-06-23
Volum
63
Pàgina inicial
35
Pàgina final
48
DOI
https://doi.org/10.1016/j.eswa.2016.06.040 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/97335 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S0957417416303219 Obrir en finestra nova
Resum
Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterati...
Citació
Askarian, M., Benitez, R., Graells, M., Zarghami, R. Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels. "Expert systems with applications", 23 Juny 2016, vol. 63, p. 35-48.
Paraules clau
Mislabeling, Label Noise, Underlying States, Operational Intelligence, Interactive Learning
Grup de recerca
CEPIMA - Centre d'Enginyeria de Processos i Medi Ambient
CREB - Centre de Recerca en Enginyeria Biomedica
SISBIO - Senyals i Sistemes Biomèdics

Participants

Arxius