Neural network and trend prediction for technological processes monitoring

Luis Paster Sanchez Fernandez; Oleksiy Pogrebnyak; Cornelio Yanez Marquez

doi:10.1007/11579427_74

Neural network and trend prediction for technological processes monitoring

Luis Paster Sanchez Fernandez, Oleksiy Pogrebnyak, Cornelio Yanez Marquez

Centro de Investigación en Computación (CIC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The goal of this paper is to introduce an efficient predictive supervisory method for the trending of variables of technological processes and devices, with low run-time, for periodic analysis of high frequency, relatively (periods smaller than a second). This method allows to predict the time in which a process variable will arrive to an abnormal or important values. The data obtained in real time for each variable are used to estimate the parameters of a mathematical model. This model is continuous and of first-order or second-order (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the parameters. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.

Original language	English
Title of host publication	MICAI 2005
Subtitle of host publication	Advances in Artificial Intelligence - 4th Mexican International Conference on Artificial Intelligence, Proceedings
Pages	731-740
Number of pages	10
DOIs	https://doi.org/10.1007/11579427_74
State	Published - 2005
Event	4th Mexican International Conference on Artificial Intelligence, MICAI 2005 - Monterrey, Mexico Duration: 14 Nov 2005 → 18 Nov 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3789 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th Mexican International Conference on Artificial Intelligence, MICAI 2005
Country/Territory	Mexico
City	Monterrey
Period	14/11/05 → 18/11/05

Access to Document

10.1007/11579427_74

Cite this

Fernandez, L. P. S., Pogrebnyak, O., & Marquez, C. Y. (2005). Neural network and trend prediction for technological processes monitoring. In MICAI 2005: Advances in Artificial Intelligence - 4th Mexican International Conference on Artificial Intelligence, Proceedings (pp. 731-740). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3789 LNAI). https://doi.org/10.1007/11579427_74

Fernandez, Luis Paster Sanchez ; Pogrebnyak, Oleksiy ; Marquez, Cornelio Yanez. / Neural network and trend prediction for technological processes monitoring. MICAI 2005: Advances in Artificial Intelligence - 4th Mexican International Conference on Artificial Intelligence, Proceedings. 2005. pp. 731-740 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Neural network and trend prediction for technological processes monitoring",

abstract = "The goal of this paper is to introduce an efficient predictive supervisory method for the trending of variables of technological processes and devices, with low run-time, for periodic analysis of high frequency, relatively (periods smaller than a second). This method allows to predict the time in which a process variable will arrive to an abnormal or important values. The data obtained in real time for each variable are used to estimate the parameters of a mathematical model. This model is continuous and of first-order or second-order (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the parameters. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.",

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Fernandez, LPS, Pogrebnyak, O & Marquez, CY 2005, Neural network and trend prediction for technological processes monitoring. in MICAI 2005: Advances in Artificial Intelligence - 4th Mexican International Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3789 LNAI, pp. 731-740, 4th Mexican International Conference on Artificial Intelligence, MICAI 2005, Monterrey, Mexico, 14/11/05. https://doi.org/10.1007/11579427_74

Neural network and trend prediction for technological processes monitoring. / Fernandez, Luis Paster Sanchez; Pogrebnyak, Oleksiy; Marquez, Cornelio Yanez.
MICAI 2005: Advances in Artificial Intelligence - 4th Mexican International Conference on Artificial Intelligence, Proceedings. 2005. p. 731-740 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3789 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Pogrebnyak, Oleksiy

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N2 - The goal of this paper is to introduce an efficient predictive supervisory method for the trending of variables of technological processes and devices, with low run-time, for periodic analysis of high frequency, relatively (periods smaller than a second). This method allows to predict the time in which a process variable will arrive to an abnormal or important values. The data obtained in real time for each variable are used to estimate the parameters of a mathematical model. This model is continuous and of first-order or second-order (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the parameters. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.

AB - The goal of this paper is to introduce an efficient predictive supervisory method for the trending of variables of technological processes and devices, with low run-time, for periodic analysis of high frequency, relatively (periods smaller than a second). This method allows to predict the time in which a process variable will arrive to an abnormal or important values. The data obtained in real time for each variable are used to estimate the parameters of a mathematical model. This model is continuous and of first-order or second-order (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the parameters. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.

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M3 - Contribución a la conferencia

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

SP - 731

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Fernandez LPS, Pogrebnyak O, Marquez CY. Neural network and trend prediction for technological processes monitoring. In MICAI 2005: Advances in Artificial Intelligence - 4th Mexican International Conference on Artificial Intelligence, Proceedings. 2005. p. 731-740. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11579427_74

Neural network and trend prediction for technological processes monitoring

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