User interface-based in machine learning as tool in the analysis of control loops performance and robustness
The monitoring of control loops in industrial processes is of great importance, considering that the correct operation of the productive procedures is related to the control loops that make up the system. Mostly, industrial processes are composed of a large amount of control loops that interact with...
- Autores:
-
Gómez Múnera, John Anderson
Jiménez-Cabas, Javier
Díaz-Charris, Luis David
- Tipo de recurso:
- Part of book
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9571
- Acceso en línea:
- https://hdl.handle.net/11323/9571
https://repositorio.cuc.edu.co/
- Palabra clave:
- Control loop performance
Performance índices
Machine learning
Neural networks
User interface
- Rights
- openAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
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User interface-based in machine learning as tool in the analysis of control loops performance and robustness |
| title |
User interface-based in machine learning as tool in the analysis of control loops performance and robustness |
| spellingShingle |
User interface-based in machine learning as tool in the analysis of control loops performance and robustness Control loop performance Performance índices Machine learning Neural networks User interface |
| title_short |
User interface-based in machine learning as tool in the analysis of control loops performance and robustness |
| title_full |
User interface-based in machine learning as tool in the analysis of control loops performance and robustness |
| title_fullStr |
User interface-based in machine learning as tool in the analysis of control loops performance and robustness |
| title_full_unstemmed |
User interface-based in machine learning as tool in the analysis of control loops performance and robustness |
| title_sort |
User interface-based in machine learning as tool in the analysis of control loops performance and robustness |
| dc.creator.fl_str_mv |
Gómez Múnera, John Anderson Jiménez-Cabas, Javier Díaz-Charris, Luis David |
| dc.contributor.author.none.fl_str_mv |
Gómez Múnera, John Anderson Jiménez-Cabas, Javier Díaz-Charris, Luis David |
| dc.subject.proposal.eng.fl_str_mv |
Control loop performance Performance índices Machine learning Neural networks User interface |
| topic |
Control loop performance Performance índices Machine learning Neural networks User interface |
| description |
The monitoring of control loops in industrial processes is of great importance, considering that the correct operation of the productive procedures is related to the control loops that make up the system. Mostly, industrial processes are composed of a large amount of control loops that interact with each other, that means both are coupled, therefore, if one of the loops does not work properly it can negatively affect the system performance, leading the other loops into setpoints that were not designed for them. It has been found that many responsible causes for poor system performance can be identified by stochastic or deterministic performance indices. These performance indices, from a theoretical perspective, allow making relevant decisions, such as design parameters adjustment of the controllers or actuators maintenance. The most known are the stochastic performance index, it requires only normal operation and knowledge of the process. However, the performance analysis in a lot of cases is not conclusive and can present scale problems. On the contrary, deterministic performance index are easier to interpret, favoring the analysis and deduction of the operator. Nevertheless, it is necessary to perform invasive tests to get them, which makes it impractical.Therefore, this work obtains a deterministic index through a inferential model built with machine learning-based neural networks that use as input the stochastic index acquired throughout recollecting the normal operational data in closed loop and in the knowledge process. furthermore, count with a graphic interface that allows the operator interactively to get performance and robustness values represented in the deterministic indices. The strategy is put on test in a real study case of sensing levels for the industrial control process FESTO® MPS-PA Compact Workstation. |
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2022 |
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2022-10-19T12:57:43Z |
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2022-10-19T12:57:43Z |
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2022 |
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Capítulo - Parte de Libro |
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http://purl.org/coar/resource_type/c_3248 |
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Gómez Múnera, J., Jiménez-Cabas, J., Díaz-Charris, L. (2022). User Interface-Based in Machine Learning as Tool in the Analysis of Control Loops Performance and Robustness. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2022. Lecture Notes in Computer Science, vol 13293. Springer, Cham. https://doi.org/10.1007/978-3-031-10539-5_16 |
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978-3-031-10538-8 |
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https://hdl.handle.net/11323/9571 |
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10.1007/978-3-031-10539-5_16 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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978-3-031-10539-5 |
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Gómez Múnera, J., Jiménez-Cabas, J., Díaz-Charris, L. (2022). User Interface-Based in Machine Learning as Tool in the Analysis of Control Loops Performance and Robustness. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2022. Lecture Notes in Computer Science, vol 13293. Springer, Cham. https://doi.org/10.1007/978-3-031-10539-5_16 978-3-031-10538-8 10.1007/978-3-031-10539-5_16 Corporación Universidad de la Costa REDICUC - Repositorio CUC 978-3-031-10539-5 |
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https://hdl.handle.net/11323/9571 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Computer Information Systems and Industrial Management;CISIM 2022 |
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Lecture Notes in Computer Science |
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Ahmad, S., Ali, S., Tabasha, R.: The design and implementation of a fuzzy gain-scheduled PID controller for the Festo MPS PA compact workstation liquid level control. Eng. Sci. Technol. Int. J. 23(2), 307–315 (2020). https://doi.org/10.1016/j.jestch.2019.05.014. https://www.sciencedirect.com/science/article/pii/S221509861831615X Bezergianni, S., Georgakis, C.: Controller performance assessment based on minimum and open-loop output variance. Control Eng. Pract. 8(7), 791–797 (2000) Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006) Bonaccorso, G.: Machine Learning Algorithms. Packt Publishing Ltd (2017) Borrero-Salazar, A.A., Cardenas-Cabrera, J.M., Barros-Gutierrez, D.A., Jimenezénez-Cabas, J.A.: A comparison study of MPC strategies based on minimum variance control index performance (2019) Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015) Bustos Pulluquitin, S.P.: Modelación matemática para un control robusto de la planta Festo MPS-PA Compact Workstation mediante la normativa IEC-61499. Ph.D. thesis, Universidad Técnica de Ambato, Ambato, Ecuador (2021). https://repositorio.uta.edu.ec/handle/123456789/32221 Cardenas-Cabrera, J., Diaz-Charris, L., Torres-Carvajal, A., Castro-Charris, N., Romero-Fandiño, E., Ruiz Ariza, J.D., Jiménez-Cabas, J.: Model predictive control strategies performance evaluation over a pipeline transportation system. J. Control Sci. Eng. 2019 (2019) Costanza, V., Rivadeneira, P.S., Gómez Múnera, J.A.: An efficient cost reduction procedure for bounded-control LQR problems. Comput. Appl. Math. 37(2), 1175–1196 (2018) Costanza, V., Rivadeneira, P.S., Gómez Múnera, J.A.: Numerical treatment of the bounded-control LQR problem by updating the final phase value. IEEE Latin Am. Trans. 14(6), 2687–2692 (2016) Desborough, L., Harris, T.: Performance assessment measures for univariate feedforward/feedback control. Can. J. Chem. Eng. 71(4), 605–616 (1993) Dorf, R.C., Bishop, R.H.: Modern Control Systems. Pearson (2011) Eriksson, P.: Some aspects of control loop performance monitoring. In: IEEE Conference of Control Applications, Glasgow, UK, 1994 (1994) Ettaleb, L.: Control loop performance assessment and oscillation detection. Ph.D. thesis, University of British Columbia (1999) Farenzena, M., Trierweiler, J.: Quantifying the impact of control loop performance, time delay and white-noise over the final product variability. In: Cancun, Mexico: International Symposium on Dynamics and Control of Process Systems (2007) Farenzena, M.: Novel methodologies for assessment and diagnostics in control loop management. Ph.D. thesis, Universidade Federal Do Rio Grande Do Sul (2008) Gómez-Múnera, J.A., Díaz-Charris, L., Ruiz-Ariza, J., Cárdenas-Cabrera, J., Ro-mero, E., Jiménez-Cabas, J.: Stochastic performance indices to infer deterministic indices through machine learning in the performance analysis of control loops. Adv. Mech. 9(3), 616–626 (2021) Gómez Múnera, J.A., Giraldo Quintero, A.: Parallel computing for rolling mill process with a numerical treatment of the LQR problem. Comput. Electron. Sci. Theor. Appli. 1(1), 11–30 (2020) Gómez Múnera, J.A., Rivadeneira Paz, P.S., Costanza, V.: A cost reduction procedure for control-restricted nonlinear systems. Int. Rev. Autom. Control (IREACO), 10 (2017) Harris, T.J.: Assessment of control loop performance. Can. J. Chem. Eng. 67(5), 856–861 (1989) Harris, T.J., Boudreau, F., MacGregor, J.F.: Performance assessment of multivariable feedback controllers. Automatica 32(11), 1505–1518 (1996) Harris, T.J., Seppala, C., Desborough, L.: A review of performance monitoring and assessment techniques for univariate and multivariate control systems. J. Process Control 9(1), 1–17 (1999) Haykin, S.: Neural Networks and Learning Machines, 3/E. Pearson Education India, Noida (2010) Huang, B., Shah, S.L.: Performance Assessment of Control Loops: Theory and Applications. Springer, Heidelberg (1999). https://doi.org/10.1007/978-1-4471-0415-5 Huang, B., Shah, S.L., Kwok, E.: Good, bad or optimal? performance assessment of multivariable processes. Automatica 33(6), 1175–1183 (1997) Jelali, M.: An overview of control performance assessment technology and industrial applications. Control Eng. Pract. 14(5), 441–466 (2006) Jelali, M.: Control Performance Management in Industrial Automation: Assessment, Diagnosis and Improvement of Control Loop Performance. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4471-4546-2 Jiménez-Cabas, J., et al.: Robust control of an evaporator through algebraic Riccati equations and D-K iteration. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11620, pp. 731–742. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24296-1_58 López Guillén, M.E.: Identificación de Sistemas. Aplicación al modelado de un motor de continua McNabb, C.A., Qin, S.J.: Projection based MIMO control performance monitoring: II–measured disturbances and setpoint changes. J. Process Control 15(1), 89–102 (2005) Moudgalya, K.M.: Digital Control. Wiley, Hoboken (2007) Ogata, K.: Modern Control Engineering. Prentice hall (2010) Qin, S.J.: Control performance monitoring-a review and assessment. Comput. Chem. Eng. 23(2), 173–186 (1998) Rivadeneira, P.S., Gómez Múnera, J.A., Costanza, V.: Dynamic allocation of industrial utilities as an optimal stochastic tracking problem. Chem. Eng. Sci. 160, 121–130 (2017) Smith, C.A., Corripio, A.B.: Principles and Practices of Automatic Process Control. Wiley, Hoboken (2005) Zhang, G.P.: A neural network ensemble method with jittered training data for time series forecasting. Inf. Sci. 177(23), 5329–5346 (2007) |
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Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AGhttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gómez Múnera, John Andersona297bd9404dcb33d002553b949cc9b65600Jiménez-Cabas, Javier66d87f68c554f0e0264e1923d8d87067600Díaz-Charris, Luis David80bedc79d81a96e1046c3c6d377d5be56002022-10-19T12:57:43Z2022-10-19T12:57:43Z2022Gómez Múnera, J., Jiménez-Cabas, J., Díaz-Charris, L. (2022). User Interface-Based in Machine Learning as Tool in the Analysis of Control Loops Performance and Robustness. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2022. Lecture Notes in Computer Science, vol 13293. Springer, Cham. https://doi.org/10.1007/978-3-031-10539-5_16978-3-031-10538-8https://hdl.handle.net/11323/957110.1007/978-3-031-10539-5_16Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/978-3-031-10539-5The monitoring of control loops in industrial processes is of great importance, considering that the correct operation of the productive procedures is related to the control loops that make up the system. Mostly, industrial processes are composed of a large amount of control loops that interact with each other, that means both are coupled, therefore, if one of the loops does not work properly it can negatively affect the system performance, leading the other loops into setpoints that were not designed for them. It has been found that many responsible causes for poor system performance can be identified by stochastic or deterministic performance indices. These performance indices, from a theoretical perspective, allow making relevant decisions, such as design parameters adjustment of the controllers or actuators maintenance. The most known are the stochastic performance index, it requires only normal operation and knowledge of the process. However, the performance analysis in a lot of cases is not conclusive and can present scale problems. On the contrary, deterministic performance index are easier to interpret, favoring the analysis and deduction of the operator. Nevertheless, it is necessary to perform invasive tests to get them, which makes it impractical.Therefore, this work obtains a deterministic index through a inferential model built with machine learning-based neural networks that use as input the stochastic index acquired throughout recollecting the normal operational data in closed loop and in the knowledge process. furthermore, count with a graphic interface that allows the operator interactively to get performance and robustness values represented in the deterministic indices. The strategy is put on test in a real study case of sensing levels for the industrial control process FESTO® MPS-PA Compact Workstation.1 páginaapplication/pdfengSpringer VerlagGermanyComputer Information Systems and Industrial Management;CISIM 2022Lecture Notes in Computer ScienceAhmad, S., Ali, S., Tabasha, R.: The design and implementation of a fuzzy gain-scheduled PID controller for the Festo MPS PA compact workstation liquid level control. Eng. Sci. Technol. Int. J. 23(2), 307–315 (2020). https://doi.org/10.1016/j.jestch.2019.05.014. https://www.sciencedirect.com/science/article/pii/S221509861831615XBezergianni, S., Georgakis, C.: Controller performance assessment based on minimum and open-loop output variance. Control Eng. Pract. 8(7), 791–797 (2000)Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)Bonaccorso, G.: Machine Learning Algorithms. Packt Publishing Ltd (2017)Borrero-Salazar, A.A., Cardenas-Cabrera, J.M., Barros-Gutierrez, D.A., Jimenezénez-Cabas, J.A.: A comparison study of MPC strategies based on minimum variance control index performance (2019)Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)Bustos Pulluquitin, S.P.: Modelación matemática para un control robusto de la planta Festo MPS-PA Compact Workstation mediante la normativa IEC-61499. Ph.D. thesis, Universidad Técnica de Ambato, Ambato, Ecuador (2021). https://repositorio.uta.edu.ec/handle/123456789/32221Cardenas-Cabrera, J., Diaz-Charris, L., Torres-Carvajal, A., Castro-Charris, N., Romero-Fandiño, E., Ruiz Ariza, J.D., Jiménez-Cabas, J.: Model predictive control strategies performance evaluation over a pipeline transportation system. J. Control Sci. Eng. 2019 (2019)Costanza, V., Rivadeneira, P.S., Gómez Múnera, J.A.: An efficient cost reduction procedure for bounded-control LQR problems. Comput. Appl. Math. 37(2), 1175–1196 (2018)Costanza, V., Rivadeneira, P.S., Gómez Múnera, J.A.: Numerical treatment of the bounded-control LQR problem by updating the final phase value. IEEE Latin Am. Trans. 14(6), 2687–2692 (2016)Desborough, L., Harris, T.: Performance assessment measures for univariate feedforward/feedback control. Can. J. Chem. Eng. 71(4), 605–616 (1993)Dorf, R.C., Bishop, R.H.: Modern Control Systems. Pearson (2011)Eriksson, P.: Some aspects of control loop performance monitoring. In: IEEE Conference of Control Applications, Glasgow, UK, 1994 (1994)Ettaleb, L.: Control loop performance assessment and oscillation detection. Ph.D. thesis, University of British Columbia (1999)Farenzena, M., Trierweiler, J.: Quantifying the impact of control loop performance, time delay and white-noise over the final product variability. In: Cancun, Mexico: International Symposium on Dynamics and Control of Process Systems (2007)Farenzena, M.: Novel methodologies for assessment and diagnostics in control loop management. Ph.D. thesis, Universidade Federal Do Rio Grande Do Sul (2008)Gómez-Múnera, J.A., Díaz-Charris, L., Ruiz-Ariza, J., Cárdenas-Cabrera, J., Ro-mero, E., Jiménez-Cabas, J.: Stochastic performance indices to infer deterministic indices through machine learning in the performance analysis of control loops. Adv. Mech. 9(3), 616–626 (2021)Gómez Múnera, J.A., Giraldo Quintero, A.: Parallel computing for rolling mill process with a numerical treatment of the LQR problem. Comput. Electron. Sci. Theor. Appli. 1(1), 11–30 (2020)Gómez Múnera, J.A., Rivadeneira Paz, P.S., Costanza, V.: A cost reduction procedure for control-restricted nonlinear systems. Int. Rev. Autom. Control (IREACO), 10 (2017)Harris, T.J.: Assessment of control loop performance. Can. J. Chem. Eng. 67(5), 856–861 (1989)Harris, T.J., Boudreau, F., MacGregor, J.F.: Performance assessment of multivariable feedback controllers. Automatica 32(11), 1505–1518 (1996)Harris, T.J., Seppala, C., Desborough, L.: A review of performance monitoring and assessment techniques for univariate and multivariate control systems. J. Process Control 9(1), 1–17 (1999)Haykin, S.: Neural Networks and Learning Machines, 3/E. Pearson Education India, Noida (2010)Huang, B., Shah, S.L.: Performance Assessment of Control Loops: Theory and Applications. Springer, Heidelberg (1999). https://doi.org/10.1007/978-1-4471-0415-5Huang, B., Shah, S.L., Kwok, E.: Good, bad or optimal? performance assessment of multivariable processes. Automatica 33(6), 1175–1183 (1997)Jelali, M.: An overview of control performance assessment technology and industrial applications. Control Eng. Pract. 14(5), 441–466 (2006)Jelali, M.: Control Performance Management in Industrial Automation: Assessment, Diagnosis and Improvement of Control Loop Performance. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4471-4546-2Jiménez-Cabas, J., et al.: Robust control of an evaporator through algebraic Riccati equations and D-K iteration. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11620, pp. 731–742. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24296-1_58López Guillén, M.E.: Identificación de Sistemas. Aplicación al modelado de un motor de continuaMcNabb, C.A., Qin, S.J.: Projection based MIMO control performance monitoring: II–measured disturbances and setpoint changes. J. Process Control 15(1), 89–102 (2005)Moudgalya, K.M.: Digital Control. Wiley, Hoboken (2007)Ogata, K.: Modern Control Engineering. Prentice hall (2010)Qin, S.J.: Control performance monitoring-a review and assessment. Comput. Chem. Eng. 23(2), 173–186 (1998)Rivadeneira, P.S., Gómez Múnera, J.A., Costanza, V.: Dynamic allocation of industrial utilities as an optimal stochastic tracking problem. Chem. Eng. Sci. 160, 121–130 (2017)Smith, C.A., Corripio, A.B.: Principles and Practices of Automatic Process Control. Wiley, Hoboken (2005)Zhang, G.P.: A neural network ensemble method with jittered training data for time series forecasting. Inf. Sci. 177(23), 5329–5346 (2007)230214https://link.springer.com/chapter/10.1007/978-3-031-10539-5_16User interface-based in machine learning as tool in the analysis of control loops performance and robustnessCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/drafthttp://purl.org/coar/version/c_b1a7d7d4d402bcceControl loop performancePerformance índicesMachine learningNeural networksUser interfaceORIGINALUser Interface-Based in Machine Learning as Tool in the Analysis of Control Loops Performance and Robustness.pdfUser Interface-Based in Machine Learning as Tool in the Analysis of Control Loops Performance and 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corporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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