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Abstract:

Many classical multivariate statistical process monitoring (MSPM) techniques assume normal distribution of the data and independence of the samples. Very often, these assumptions do not hold for real industrial chemical processes, where multiple plant operating modes lead to multiple nominal operation regions. MSPM techniques that do not take account of this fact show increased false alarm and missing alarm rates. In this work, a simple fault detection tool based on a robust clustering technique is implemented to detect abnormal situations in an industrial installation with multiple operation modes. The tool is applied to three case studies: (i) a two-dimensional toy example, (ii) a realistic simulation usually used as a benchmark example, known as the Tennessee-Eastman Process, and (iii) real data from a methanol plant. The clustering technique on which the tool relies assumes that the observations come from multiple populations with a common covariance matrix (i.e., the same underlying physical relations). The clustering technique is also capable of coping with a certain percentage of outliers, thus avoiding the need of extensive preprocessing of the data. Moreover, improvements in detection capacity are found when comparing the results to those obtained with standard methodologies. Hence, the feasibility of implementing fault detection tools based on this technique in the field of chemical industrial processes is discussed. © 2009 Elsevier Ltd. All rights reserved.

Registro:

Documento: Artículo
Título:A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes
Autor:Maestri, M.; Farall, A.; Groisman, P.; Cassanello, M.; Horowitz, G.
Filiación:PINMATE, Dep. de Industrias, FCEyN, C1428BGA Buenos Aires, Argentina
Instituto de Cálculo, FCEyN, Universidad de Buenos Aires, Argentina
Centro de Tecnología Argentina, YPF, Argentina
Palabras clave:Fault detection; Multiple operating modes; Multivariate statistical process monitoring; Alarm rate; Clustering techniques; False alarms; Industrial installations; Industrial processs; Methanol plants; Multiple operations; Multivariate statistical process monitoring; Nominal operations; Operating modes; Realistic simulation; Robust clustering; Steady-state operation; Tennessee Eastman process; Chemical detection; Cluster analysis; Covariance matrix; Inspection equipment; Methanol; Multivariant analysis; Normal distribution; Process monitoring; Statistical process control; Fault detection
Año:2010
Volumen:34
Número:2
Página de inicio:223
Página de fin:231
DOI: http://dx.doi.org/10.1016/j.compchemeng.2009.05.012
Título revista:Computers and Chemical Engineering
Título revista abreviado:Comput. Chem. Eng.
ISSN:00981354
CODEN:CCEND
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00981354_v34_n2_p223_Maestri

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Citas:

---------- APA ----------
Maestri, M., Farall, A., Groisman, P., Cassanello, M. & Horowitz, G. (2010) . A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes. Computers and Chemical Engineering, 34(2), 223-231.
http://dx.doi.org/10.1016/j.compchemeng.2009.05.012
---------- CHICAGO ----------
Maestri, M., Farall, A., Groisman, P., Cassanello, M., Horowitz, G. "A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes" . Computers and Chemical Engineering 34, no. 2 (2010) : 223-231.
http://dx.doi.org/10.1016/j.compchemeng.2009.05.012
---------- MLA ----------
Maestri, M., Farall, A., Groisman, P., Cassanello, M., Horowitz, G. "A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes" . Computers and Chemical Engineering, vol. 34, no. 2, 2010, pp. 223-231.
http://dx.doi.org/10.1016/j.compchemeng.2009.05.012
---------- VANCOUVER ----------
Maestri, M., Farall, A., Groisman, P., Cassanello, M., Horowitz, G. A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes. Comput. Chem. Eng. 2010;34(2):223-231.
http://dx.doi.org/10.1016/j.compchemeng.2009.05.012