Intelligent data analysis to calculate the operational reliability coefficient

Zoila Esther Morales Tabares; Alcides Cabrera Campos; Efrén Vázquez Silva; Roberto Antonio Infante Milanés

doi:10.1007/978-3-030-01132-1_8

Intelligent data analysis to calculate the operational reliability coefficient

Zoila Esther Morales Tabares, Alcides Cabrera Campos, Efrén Vázquez Silva, Roberto Antonio Infante Milanés

Research Group in New Materials and Transformation Processes (GIMAT)

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

6 Scopus citations

Abstract

Nowadays the complexity that medical equipment has reached means that not all failure patterns can be easily managed through maintenance activities, carried out after their manufacture and commissioning. For this reason, experts in electromedicine consider that the analysis of failure patterns should be carried out with the tools of reliability engineering, since medical equipment is a technology that is not without risks. Failures in these devices are caused by risks associated mainly with operator malfunctions, impairment of the electrical fluid that causes the stopping of procedures in execution in an unexpected manner and others inherent to the technology. All these risks lead to a dynamic working behaviour of medical equipment, which passes through a finite number of states: running, faulty and broken. As part of the analysis of failure patterns in medical equipment, the CONFEM algorithm is proposed in this manuscript to determine the operational reliability coefficient.

Original language	English
Title of host publication	Progress in Artificial Intelligence and Pattern Recognition - 6th International Workshop, IWAIPR 2018, Proceedings
Editors	Yanio Hernández Heredia, Vladimir Milián Núñez, José Ruiz Shulcloper
Publisher	Springer Verlag
Pages	68-76
Number of pages	9
ISBN (Print)	9783030011314
DOIs	https://doi.org/10.1007/978-3-030-01132-1_8
State	Published - 1 Jan 2018
Event	6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018 - Havana, Cuba Duration: 24 Sep 2018 → 26 Sep 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11047 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018
Country/Territory	Cuba
City	Havana
Period	24/09/18 → 26/09/18

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

Keywords

Algorithm
Failure patterns
Medical equipment
Operational reliability coefficient
Risks

Access to Document

10.1007/978-3-030-01132-1_8

Cite this

Morales Tabares, Z. E., Cabrera Campos, A., Vázquez Silva, E., & Infante Milanés, R. A. (2018). Intelligent data analysis to calculate the operational reliability coefficient. In Y. H. Heredia, V. M. Núñez, & J. R. Shulcloper (Eds.), Progress in Artificial Intelligence and Pattern Recognition - 6th International Workshop, IWAIPR 2018, Proceedings (pp. 68-76). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11047 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01132-1_8

Morales Tabares, Zoila Esther ; Cabrera Campos, Alcides ; Vázquez Silva, Efrén et al. / Intelligent data analysis to calculate the operational reliability coefficient. Progress in Artificial Intelligence and Pattern Recognition - 6th International Workshop, IWAIPR 2018, Proceedings. editor / Yanio Hernández Heredia ; Vladimir Milián Núñez ; José Ruiz Shulcloper. Springer Verlag, 2018. pp. 68-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Morales Tabares, ZE, Cabrera Campos, A, Vázquez Silva, E & Infante Milanés, RA 2018, Intelligent data analysis to calculate the operational reliability coefficient. in YH Heredia, VM Núñez & JR Shulcloper (eds), Progress in Artificial Intelligence and Pattern Recognition - 6th International Workshop, IWAIPR 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11047 LNCS, Springer Verlag, pp. 68-76, 6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018, Havana, Cuba, 24/09/18. https://doi.org/10.1007/978-3-030-01132-1_8

Intelligent data analysis to calculate the operational reliability coefficient. / Morales Tabares, Zoila Esther; Cabrera Campos, Alcides; Vázquez Silva, Efrén et al.
Progress in Artificial Intelligence and Pattern Recognition - 6th International Workshop, IWAIPR 2018, Proceedings. ed. / Yanio Hernández Heredia; Vladimir Milián Núñez; José Ruiz Shulcloper. Springer Verlag, 2018. p. 68-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11047 LNCS).

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

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AU - Morales Tabares, Zoila Esther

AU - Cabrera Campos, Alcides

AU - Vázquez Silva, Efrén

AU - Infante Milanés, Roberto Antonio

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018/1/1

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N2 - Nowadays the complexity that medical equipment has reached means that not all failure patterns can be easily managed through maintenance activities, carried out after their manufacture and commissioning. For this reason, experts in electromedicine consider that the analysis of failure patterns should be carried out with the tools of reliability engineering, since medical equipment is a technology that is not without risks. Failures in these devices are caused by risks associated mainly with operator malfunctions, impairment of the electrical fluid that causes the stopping of procedures in execution in an unexpected manner and others inherent to the technology. All these risks lead to a dynamic working behaviour of medical equipment, which passes through a finite number of states: running, faulty and broken. As part of the analysis of failure patterns in medical equipment, the CONFEM algorithm is proposed in this manuscript to determine the operational reliability coefficient.

AB - Nowadays the complexity that medical equipment has reached means that not all failure patterns can be easily managed through maintenance activities, carried out after their manufacture and commissioning. For this reason, experts in electromedicine consider that the analysis of failure patterns should be carried out with the tools of reliability engineering, since medical equipment is a technology that is not without risks. Failures in these devices are caused by risks associated mainly with operator malfunctions, impairment of the electrical fluid that causes the stopping of procedures in execution in an unexpected manner and others inherent to the technology. All these risks lead to a dynamic working behaviour of medical equipment, which passes through a finite number of states: running, faulty and broken. As part of the analysis of failure patterns in medical equipment, the CONFEM algorithm is proposed in this manuscript to determine the operational reliability coefficient.

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BT - Progress in Artificial Intelligence and Pattern Recognition - 6th International Workshop, IWAIPR 2018, Proceedings

A2 - Heredia, Yanio Hernández

A2 - Núñez, Vladimir Milián

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ER -

Morales Tabares ZE, Cabrera Campos A, Vázquez Silva E, Infante Milanés RA. Intelligent data analysis to calculate the operational reliability coefficient. In Heredia YH, Núñez VM, Shulcloper JR, editors, Progress in Artificial Intelligence and Pattern Recognition - 6th International Workshop, IWAIPR 2018, Proceedings. Springer Verlag. 2018. p. 68-76. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-01132-1_8

Intelligent data analysis to calculate the operational reliability coefficient

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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