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oai:recercat.cat:2117/973352026-01-16T10:10:18Zcom_2072_1033col_2072_452950

Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels Askarian, Mahdieh Benítez Iglesias, Raúl Graells Sobré, Moisès Zarghami, Reza Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial Universitat Politècnica de Catalunya. Departament d'Enginyeria Química Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics Universitat Politècnica de Catalunya. CEPIMA - Center for Process and Environment Engineering Àrees temàtiques de la UPC::Enginyeria química Chemical processes Mislabeling Label noise Underlying states Operational intelligence Interactive learning Processos químics 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 iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problem Peer Reviewed Postprint (author's final draft) 2016-06-23 Article 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. 0957-4174 https://hdl.handle.net/2117/97335 10.1016/j.eswa.2016.06.040 eng http://www.sciencedirect.com/science/article/pii/S0957417416303219 http://creativecommons.org/licenses/by-nc-nd/3.0/es/ Open Access 14 p. application/pdf