Neural network based industrial processes monitoring

Luis P. Sánchez-Fernández; Cornelio Yáñez-Márquez; Oleksiy Pogrebnyak

doi:10.1007/11760191_136

Neural network based industrial processes monitoring

Luis P. Sánchez-Fernández, Cornelio Yáñez-Márquez, Oleksiy Pogrebnyak

Centro de Investigación en Computación (CIC)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

This industrial processes monitoring based on a neural network presents low run-time, and it useful for critical time tasks with periodic processing, This method allows the time prediction in which a variable will arrive to abnormal or important values. The data of each variable are used to estimate the parameters of a continuous mathematical model. At this moment, four models are used: first-order or second-order in three types (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the model parameters for a dynamic response to step input function, because this is the most frequent disturbance. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.

Idioma original	Inglés
Título de la publicación alojada	Advances in Neural Networks - ISNN 2006
Subtítulo de la publicación alojada	Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III
Editorial	Springer Verlag
Páginas	933-938
Número de páginas	6
ISBN (versión impresa)	3540344829, 9783540344827
DOI	https://doi.org/10.1007/11760191_136
Estado	Publicada - 2006
Evento	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China Duración: 28 may. 2006 → 1 jun. 2006

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	3973 LNCS
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
País/Territorio	China
Ciudad	Chengdu
Período	28/05/06 → 1/06/06

Acceder al documento

10.1007/11760191_136

Otros archivos y enlaces

Enlace a la publicación en Scopus

Citar esto

Sánchez-Fernández, L. P., Yáñez-Márquez, C., & Pogrebnyak, O. (2006). Neural network based industrial processes monitoring. En Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III (pp. 933-938). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3973 LNCS). Springer Verlag. https://doi.org/10.1007/11760191_136

Sánchez-Fernández, Luis P. ; Yáñez-Márquez, Cornelio ; Pogrebnyak, Oleksiy. / Neural network based industrial processes monitoring. Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag, 2006. pp. 933-938 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{81b5b6effca448c3bfad1b742e749373,

title = "Neural network based industrial processes monitoring",

abstract = "This industrial processes monitoring based on a neural network presents low run-time, and it useful for critical time tasks with periodic processing, This method allows the time prediction in which a variable will arrive to abnormal or important values. The data of each variable are used to estimate the parameters of a continuous mathematical model. At this moment, four models are used: first-order or second-order in three types (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the model parameters for a dynamic response to step input function, because this is the most frequent disturbance. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.",

author = "S{\'a}nchez-Fern{\'a}ndez, {Luis P.} and Cornelio Y{\'a}{\~n}ez-M{\'a}rquez and Oleksiy Pogrebnyak",

year = "2006",

doi = "10.1007/11760191_136",

language = "Ingl{\'e}s",

isbn = "3540344829",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "933--938",

booktitle = "Advances in Neural Networks - ISNN 2006",

address = "Alemania",

note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",

}

Sánchez-Fernández, LP , Yáñez-Márquez, C & Pogrebnyak, O 2006, Neural network based industrial processes monitoring. En Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3973 LNCS, Springer Verlag, pp. 933-938, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, China, 28/05/06. https://doi.org/10.1007/11760191_136

Neural network based industrial processes monitoring. / Sánchez-Fernández, Luis P.; Yáñez-Márquez, Cornelio; Pogrebnyak, Oleksiy.
Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag, 2006. p. 933-938 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3973 LNCS).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Neural network based industrial processes monitoring

AU - Sánchez-Fernández, Luis P.

AU - Yáñez-Márquez, Cornelio

AU - Pogrebnyak, Oleksiy

PY - 2006

Y1 - 2006

N2 - This industrial processes monitoring based on a neural network presents low run-time, and it useful for critical time tasks with periodic processing, This method allows the time prediction in which a variable will arrive to abnormal or important values. The data of each variable are used to estimate the parameters of a continuous mathematical model. At this moment, four models are used: first-order or second-order in three types (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the model parameters for a dynamic response to step input function, because this is the most frequent disturbance. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.

AB - This industrial processes monitoring based on a neural network presents low run-time, and it useful for critical time tasks with periodic processing, This method allows the time prediction in which a variable will arrive to abnormal or important values. The data of each variable are used to estimate the parameters of a continuous mathematical model. At this moment, four models are used: first-order or second-order in three types (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the model parameters for a dynamic response to step input function, because this is the most frequent disturbance. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.

UR - http://www.scopus.com/inward/record.url?scp=33745886219&partnerID=8YFLogxK

U2 - 10.1007/11760191_136

DO - 10.1007/11760191_136

M3 - Contribución a la conferencia

SN - 3540344829

SN - 9783540344827

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 933

EP - 938

BT - Advances in Neural Networks - ISNN 2006

PB - Springer Verlag

T2 - 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

Y2 - 28 May 2006 through 1 June 2006

ER -

Sánchez-Fernández LP , Yáñez-Márquez C, Pogrebnyak O. Neural network based industrial processes monitoring. En Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag. 2006. p. 933-938. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11760191_136

Neural network based industrial processes monitoring

Resumen

Serie de la publicación

Conferencia

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto