Mostra el registre d'ítem simple
Fault diagnosis of chemical processes with incomplete observations: A comparative study
dc.contributor.author | Askarian, Mahdieh |
dc.contributor.author | Escudero Bakx, Gerard |
dc.contributor.author | Graells Sobré, Moisès |
dc.contributor.author | Zarghami, Reza |
dc.contributor.author | Jalali Farahani, Farhang |
dc.contributor.author | Mostoufi, Navid |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Química |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2016-01-26T11:08:16Z |
dc.date.available | 2017-01-03T01:30:36Z |
dc.date.issued | 2016-01-04 |
dc.identifier.citation | Askarian, M., Escudero, G., Graells, M., Zarghami, R., Jalali Farahani, F., Mostoufi, N. Fault diagnosis of chemical processes with incomplete observations: A comparative study. "Computers & chemical engineering", 04 Gener 2016, vol. 84, p. 104-116. |
dc.identifier.issn | 0098-1354 |
dc.identifier.uri | http://hdl.handle.net/2117/82031 |
dc.description.abstract | An important problem to be addressed by diagnostic systems in industrial applications is the estimation of faults with incomplete observations. This work discusses different approaches for handling missing data, and performance of data-driven fault diagnosis schemes. An exploiting classifier and combined methods were assessed in Tennessee-Eastman process, for which diverse incomplete observations were produced. The use of several indicators revealed the trade-off between performances of the different schemes. Support vector machines (SVM) and C4.5, combined with k-nearest neighbourhood (kNN), produce the highest robustness and accuracy, respectively. Bayesian networks (BN) and centroid appear as inappropriate options in terms of accuracy, while Gaussian naive Bayes (GNB) is sensitive to imputation values. In addition, feature selection was explored for further performance enhancement, and the proposed contribution index showed promising results. Finally, an industrial case was studied to assess informative level of incomplete data in terms of the redundancy ratio and generalize the discussion. (C) 2015 Elsevier Ltd. All rights reserved. |
dc.format.extent | 13 p. |
dc.language.iso | eng |
dc.publisher | Pergamon Press |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria química |
dc.subject.lcsh | Chemical processes |
dc.subject.other | Fault diagnosis |
dc.subject.other | Missing data |
dc.subject.other | Incomplete observations |
dc.subject.other | Classification |
dc.subject.other | Imputation |
dc.subject.other | Machine learning |
dc.subject.other | issing data |
dc.subject.other | tolerant control |
dc.subject.other | soft sensors |
dc.subject.other | classification |
dc.subject.other | inference |
dc.subject.other | information |
dc.subject.other | algorithms |
dc.subject.other | values |
dc.subject.other | pls |
dc.subject.other | pca |
dc.title | Fault diagnosis of chemical processes with incomplete observations: A comparative study |
dc.type | Article |
dc.subject.lemac | Processos químics -- Gestió |
dc.contributor.group | Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural |
dc.contributor.group | Universitat Politècnica de Catalunya. CEPIMA - Center for Process and Environment Engineering |
dc.identifier.doi | 10.1016/j.compchemeng.2015.08.018 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S0098135415002793 |
dc.rights.access | Open Access |
local.identifier.drac | 16977971 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MICINN//DPI2009-09386/ES/Extendiendo Los Horizontes Productivos Frente A La Paradoja De La Integracion/ |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/1PE/DPI2012-37154-C02-01 |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/2014SGR1092 |
local.citation.author | Askarian, M.; Escudero, G.; Graells, M.; Zarghami, R.; Jalali Farahani, F.; Mostoufi, N. |
local.citation.publicationName | Computers & chemical engineering |
local.citation.volume | 84 |
local.citation.startingPage | 104 |
local.citation.endingPage | 116 |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Articles de revista [1.049]
-
Articles de revista [97]
-
Articles de revista [128]
-
Articles de revista [2.227]