- -

Arquitectura de referencia para el diseño y desarrollo de aplicaciones para la Industria 4.0

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Arquitectura de referencia para el diseño y desarrollo de aplicaciones para la Industria 4.0

Mostrar el registro completo del ítem

Dintén, R.; López Martínez, P.; Zorrilla, M. (2021). Arquitectura de referencia para el diseño y desarrollo de aplicaciones para la Industria 4.0. Revista Iberoamericana de Automática e Informática industrial. 18(3):300-311. https://doi.org/10.4995/riai.2021.14532

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/168916

Ficheros en el ítem

Metadatos del ítem

Título: Arquitectura de referencia para el diseño y desarrollo de aplicaciones para la Industria 4.0
Otro titulo: Reference architecture for the design and development of applications for Industry 4.0
Autor: Dintén, R. López Martínez, P. Zorrilla, M.
Fecha difusión:
Resumen:
[EN] The real implementation of Industry 4.0 requires the reformulation and coordination of industrial processes. This requires defining a digital platform that integrates and facilitates communication and interaction ...[+]


[ES] La implementación práctica de la Industria 4.0 requiere la reformulación y coordinación de los procesos industriales. Para ello se requiere disponer de una plataforma digital que integre y facilite la comunicación e ...[+]
Palabras clave: Data-centric architecture , Metamodel , Model-based development , Industrial applications , Industry 4.0 , Arquitectura centrada en el dato , Metamodelo , Desarrollo basado en modelos , Aplicaciones industriales , Industria 4.0
Derechos de uso: Reconocimiento - No comercial - Compartir igual (by-nc-sa)
Fuente:
Revista Iberoamericana de Automática e Informática industrial. (issn: 1697-7912 ) (eissn: 1697-7920 )
DOI: 10.4995/riai.2021.14532
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/riai.2021.14532
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-86520-C3-3-R/ES/SISTEMAS INFORMATICOS PREDECIBLES Y CONFIABLES PARA LA INDUSTRIA 4.0/
Agradecimientos:
Este trabajo ha sido financiado en parte por el Gobierno de España y los fondos FEDER (AEI/FEDER, UE) en el proyecto TIN2017-86520-C3-3-R (PRECON-I4).
Tipo: Artículo

References

Ahmad, S., Badwelan, A., Ghaleb, A. M., Qamhan, A., Sharaf, M. Analyzing critical failures in a production process: is industrial iot the solution?, Wireless Communications and Mobile Computing (2018). https://doi.org/10.1155/2018/6951318

Alcácer, V., Cruz-Machado, V. Scanning the industry 4.0: A literature review on technologies for manufacturing systems, Engineering Science and Technology, an International Journal 22 (3) (2019) 899 - 919. https://doi.org/10.1016/j.jestch.2019.01.006

Angulo, P., Guzmán, C. C., Jiménez, G., Romero, D. A service-oriented architecture and its ict-infrastructure to support eco-efficiency performance monitoring in manufacturing enterprises, International Journal of Computer Integrated Manufacturing 30 (1) (2017) 202-214. arXiv:https://www.tandfonline.com/doi/pdf/10.1080/0951192X.2016.1145810, https://doi.org/10.1080/0951192X.2016.1145810 [+]
Ahmad, S., Badwelan, A., Ghaleb, A. M., Qamhan, A., Sharaf, M. Analyzing critical failures in a production process: is industrial iot the solution?, Wireless Communications and Mobile Computing (2018). https://doi.org/10.1155/2018/6951318

Alcácer, V., Cruz-Machado, V. Scanning the industry 4.0: A literature review on technologies for manufacturing systems, Engineering Science and Technology, an International Journal 22 (3) (2019) 899 - 919. https://doi.org/10.1016/j.jestch.2019.01.006

Angulo, P., Guzmán, C. C., Jiménez, G., Romero, D. A service-oriented architecture and its ict-infrastructure to support eco-efficiency performance monitoring in manufacturing enterprises, International Journal of Computer Integrated Manufacturing 30 (1) (2017) 202-214. arXiv:https://www.tandfonline.com/doi/pdf/10.1080/0951192X.2016.1145810, https://doi.org/10.1080/0951192X.2016.1145810

Arantes, M., Bonnard, R., Mattei, A. P., Saqui-Sannes, P. de. General architecture for data analysis in industry 4.0 using sysml and model based system engineering, in: 2018 Annual IEEE International Systems Conference, SysCon 2018, Vancouver, BC, Canada, April 23-26, 2018, 2018, pp.1-6. https://doi.org/10.1109/SYSCON.2018.8369574

Arantes, M., Bonnard, R., Mattei, A. P., Saqui-Sannes, P. de. General architecture for data analysis in industry 4.0 using sysml and model based system engineering, in: 2018 Annual IEEE International Systems Conference (SysCon), 2018, pp. 1-6. https://doi.org/10.1109/SYSCON.2018.8369574

The apache avro project: a data serialization system, http://avro. apache.org (accessed 30 April 2019).

Apache Cassandra., http://cassandra.apache.org/ (accessed 30 April 2019).

Apache Kafka project: A distributed streaming platform, http://kafka. apache.org/ (accessed 30 April 2019). The Apache Software Foundation, http://www.apache.org/ (accessed 30 April 2019).

Apache Spark: A fast and general engine for large-scale data processing, http://spark.apache.org/ (accessed 30 Dec 2019).

Apache Storm: A fast and general engine for large-scale data processing, https://storm.apache.org/ (accessed 30 Dec 2019) Apache Zookeeper., https://zookeeper.apache.org/ (accessed 30 April 2019).

Apache Zookeeper., https://zookeeper.apache.org/ (accessed 30 April 2019).

Belman-López, C., Jiménez-García, J., & Hernández-González, S. (2020). Análisis exhaustivo de los principios de diseño en el contexto de Industria 4.0, Revista Iberoamericana de Automática e Informática industrial, 17(4), 432-447. https://doi.org/10.4995/riai.2020.12579

Chen, Y., Feng, Q., Shi, W. An industrial robot system based on edge computing: An early experience, in: USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18), USENIX Association, Boston, MA, 2018.

Díaz, G., Macià, H., Valero, V., Boubeta-Puig, J., Cuartero, F, An Intelligent Transportation System to control air pollution and road traffic in cities integrating CEP and Colored Petri Nets. Neural Computing and Applications 32(2): 405-426 (2020). https://doi.org/10.1007/s00521-018-3850-1

Empowering app development for developers | Docker, https://www.docker.com/ (accessed 28 September 2020)

Ghobakhloo, M. The future of manufacturing industry: a strategic roadmap toward industry 4.0, Journal of Manufacturing Technology Management 29 (2018) 910-936. https://doi.org/10.1108/JMTM-02-2018-0057

Guerriero, M., Tajfar, S., Tamburri, D. A., Di Nitto, E. Towards a model- driven design tool for big data architectures, in: Proceedings of the 2Nd International Workshop on BIG Data Software Engineering, BIGDSE '16, ACM, New York, NY, USA, 2016, pp. 37-43. https://doi.org/10.1145/2896825

Hermann, M., Pentek, T., Otto, B. Design principles for industrie 4.0 scenarios, in: 2016 49th Hawaii International Conference on System Sciences (HICSS), 2016, pp. 3928-3937. https://doi.org/10.1109/HICSS.2016.488

I. I. Consortium, Industrial internet reference architecture v1.9, http://www.iiconsortium.org/IIRA.htm, accessed 30 April 2019 (2019).

Junqueira. F., Reed B., ZooKeeper: Distributed process Coordination, O,Reilly, 2014.

Kubernetes., https://kubernetes.io/ (accessed 18 Decemeber 2020)

Marino F., Seitanidis I., Dao P., Bocchino S., Castoldi P., Salvadori C. IoT enabling PI: towards hyperconnected and interoperable smart containers, 6th International Physical Internet Conference, 2019, pp. 349-362.

Pérez-Palacín, D., Merseguer, J., Requeno, J. I., Guerriero, M., Di Nitto, E., Tamburri, D. A. A uml profile for the design, quality assessment and de- ployment of data-intensive applications, Software and Systems Modeling 18 (6) (2019) 3577-3614. https://doi.org/10.1007/s10270-019-00730-3

Petrasch, R., Hentschke, R. Process modeling for industry 4.0 applications: Towards an industry 4.0 process modeling language and method, in: 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2016, pp. 1-5. https://doi.org/10.1109/JCSSE.2016.7748885

RAMI 4.0, Reference architectural model industrie 4.0, https://www. plattform-i40.de/PI40/Redaktion/EN/Downloads/Publikation/rami40-an-introduction.html, accessed 30 Dec 2019 (2018).

Prometheus exporters, https://github.com/prometheus/node_exporter (accessed 30 April 2019).

Prometheus overview, https://prometheus.io/docs/introduction/overview/ (accessed 30 April 2019).

RAI4 deployment tool and metamodel, https://github.com/istr-uc/RAI4DeploymentTool (accessed 20 July 2020).

Rajbhoj, A., Kulkarni, V., Bellarykar, N. Early experience with model-driven development of mapreduce based big data application, in: 2014 21st Asia- Pacific Software Engineering Conference, Vol. 1, 2014, pp. 94-97. https://doi.org/10.1109/APSEC.2014.23

Raptis, T. P., Passarella, A., Conti, M. Data management in industry 4.0: State of the art and open challenges, IEEE Access 7 (2019) 97052-97093. https://doi.org/10.1109/ACCESS.2019.2929296

Reza Delavar, M., Gholami, A., Reza Shiran, G., Rashidi, Y., Reza Nakhaeizadeh, G., Kurt Freda, Smaeil Hatefi Afshar, "A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran". ISPRS Int. J. Geo-Information 8(2): 99m 2019. https://doi.org/10.3390/ijgi8020099

Sahal, R., Breslin, J. G., Ali, M. I. Big data and stream processing platforms for industry 4.0 requirements mapping for a predictive maintenance use case, Journal of Manufacturing Systems 54 (2020) 138 - 151. https://doi.org/10.1016/j.jmsy.2019.11.004

Salkin, C., Oner, M., Ustundag, A., Cevikcan, E. A Conceptual Framework for Industry 4.0, Springer International Publishing, Cham, 2018, pp. 3-23. https://doi.org/10.1007/978-3-319-57870-5_1

Thoben, K.-D., Wiesner, S., Wuest, T. "industrie 4.0" and smart manufacturing - a review of research issues and application examples, International Journal of Automation Technology 11 (1) (2017) 4-16. https://doi.org/10.20965/ijat.2017.p0004

Ungurean, I., Gaitan, N.C. A Software Architecture for the Industrial Internet of Things-A Conceptual Model, Sensors 2020, 20, 5603. https://doi.org/10.3390/s20195603

Velásquez, N., Estevez, E., Pesado, P. Cloud computing, big data and the industry 4.0 reference architectures, Journal of Computer Science and Technology 18 (03) (2018) e29. https://doi.org/10.24215/16666038.18.e29

Wiesner, S., Thoben, K.-D. Requirements for models, methods and tools supporting servitisation of products in manufacturing service ecosystems, International Journal of Computer Integrated Manufacturing (2016) 1- 11. https://doi.org/10.1080/0951192X.2015.1130243

Wingerath, W., Gessert, F., Friedrich, S., Ritter, N. "Real-time stream processing for big data", Information Technology 4 (58) (2016) 186-194. https://doi.org/10.1515/itit-2016-0002

Wortmann, A., Combemale, B., Barais, O. A systematic mapping study on modeling for industry 4.0, in: 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS), 2017, pp. 281-291. https://doi.org/10.1109/MODELS.2017.14

Yebenes, J., Zorrilla, M. Towards a data governance framework for third generation platforms, Procedia Computer Science The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40) (2019).

Zhong, R. Y., Xu, X., Klotz, E., Newman, S. T. Intelligent manufacturing in the context of industry 4.0: A review, Engineering 3 (5) (2017) 616 - 630. https://doi.org/10.1016/J.ENG.2017.05.015

Zorrilla, M. E., Ibrain, Á. Bernard, an energy intelligent system for raising residential users awareness, Computers & Industrial Engineering 135 (2019) 492-499. https://doi.org/10.1016/j.cie.2019.06.040

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem