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Detection of operational failures with artificial neural networks: application to the Tennessee Eastman process

Detección de fallas operacionales con redes neuronales artificiales: aplicación del proceso Tennessee Eastman

dc.contributor.authorMorales, Giovanni
dc.contributor.authorReyes Angarita, Sebastian
dc.date.accessioned2024-11-01T13:48:27Z
dc.date.available2024-11-01T13:48:27Z
dc.date.created2023-03-01
dc.identifier.issn1692-3324
dc.identifier.urihttp://hdl.handle.net/11407/8629
dc.descriptionThe purpose of this article is to compare results of fault detection for the Tennessee Eastman (TE) process with the application of artificial neural networks (ANN). The Neuralnet library of the open-source program R, as well as the Keras library of the open-source program Python were used for the training of ANN. The TE process simulation data were downloaded from Harvard University’s server, and subsequently analyzed, defining the trends in the operational variables during the appearance of failures. With the database, the training and validation of different ANN structures were developed, considering the parameters number of hidden neurons, activation function, and number of hidden layers. According to the results, the training and validation of the ANNs with the Neuralnet library yielded a lower performance in fault detection than that obtained with the Keras library. The ANN with the best performance in detecting failures in the TE process was obtained by the application of the Keras library. This ANN considered 52 input variables, 11 neurons in the hidden layer, and one neuron in the output layer, using a logistic function (ANN represented as 52:11:1 logistic) and reporting a prediction efficiency of 92% for the detection of faults with an external test set, which is convenient for future implementation in industrial processes.eng
dc.descriptionEste artículo tiene como finalidad la comparación de resultados de detección de fallas en el proceso Tennessee Eastman (TE) con redes neuronales artificiales (RNA), utilizando las librerías neuralnet del programa de código abierto R y Keras del programa de código abierto Python. Para esto, los datos de la simulación de proceso TE fueron descargados del servidor de la universidad de Harvard, y posteriormente analizados, definiendo las tendencias en las variables operacionales ante las respectivas fallas. Con la base de datos, el entrenamiento y la validación de diferentes estructuras de RNA fue desarrollado considerando los parámetros: número de neuronas ocultas, función de activación y número de capas ocultas. Según los resultados, el entrenamiento y la validación de las RNA con la librería neuralnet reportó menores desempeños de detección de fallas, que las obtenidas con la librería Keras. La RNA de mejor desempeño en la detección de fallas del proceso TE correspondió a la estructura 52 variables de entrada, 11 neuronas en la capa oculta y una neurona en la capa de salida, con función logística y entrenada con la librería Keras (RNA representada como 52:11:1 logistic). Esta RNA presenta una eficiencia en la predicción del 92% para la detección de fallas en un conjunto externo de prueba, lo que resulta conveniente en una futura implementación en procesos industriales.spa
dc.format.extentp. 1-19
dc.format.mediumElectrónico
dc.format.mimetypePDF
dc.language.isoeng
dc.publisherUniversidad de Medellín
dc.relation.ispartofseriesRevista Ingenierías Universidad de Medellín; Vol. 23 No. 44 (2024)
dc.relation.haspartRevista Ingenierías Universidad de Medellín; Vol. 23 Núm. 44 enero-junio 2024
dc.relation.urihttps://revistas.udem.edu.co/index.php/ingenierias/article/view/4622
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0*
dc.sourceRevista Ingenierías Universidad de Medellín; Vol. 22 No. 42 (2023): (enero-junio); 1-19
dc.subjectArtificial neural networkeng
dc.subjectTennessee Eastman processeng
dc.subjectFaultseng
dc.subjectFault detectioneng
dc.subjectFault predictioneng
dc.subjectProcess dataeng
dc.subjecttraining of ANNeng
dc.subjectNeuralneteng
dc.subjectKeraseng
dc.subjectRed neuronal artificialspa
dc.subjectProceso Tennessee Eastmanspa
dc.subjectFallasspa
dc.subjectDetección de fallasspa
dc.subjectPredicción de fallasspa
dc.subjectDatos de procesospa
dc.subjectEntrenamiento de RNAspa
dc.subjectNeuralnetspa
dc.subjectKerasspa
dc.titleDetection of operational failures with artificial neural networks: application to the Tennessee Eastman processeng
dc.titleDetección de fallas operacionales con redes neuronales artificiales: aplicación del proceso Tennessee Eastmanspa
dc.typearticle
dc.identifier.doihttps://doi.org/10.22395/rium.v23n44a1
dc.relation.citationvolume23
dc.relation.citationissue44
dc.relation.citationstartpage1
dc.relation.citationendpage19
dc.audienceComunidad Universidad de Medellín
dc.publisher.facultyFacultad de Ingenierías
dc.coverageLat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degreesLong: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees
dc.publisher.placeMedellín
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dc.rights.creativecommonsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.identifier.eissn2248-4094
dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.localArtículo científico
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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