- -

Diseño de una arquitectura para sistemas y aplicaciones en Industria 4.0 basada en computación en la nube y análisis de datos

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Diseño de una arquitectura para sistemas y aplicaciones en Industria 4.0 basada en computación en la nube y análisis de datos

Mostrar el registro completo del ítem

Belman-López, CE.; Jiménez-García, JA.; Vázquez-Lopez, JA.; Camarillo-Gómez, KA. (2023). Diseño de una arquitectura para sistemas y aplicaciones en Industria 4.0 basada en computación en la nube y análisis de datos. Revista Iberoamericana de Automática e Informática industrial. 20(2):137-149. https://doi.org/10.4995/riai.2022.17791

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/192797

Ficheros en el ítem

Thumbnail
Nombre: Belman-LopezJimen ...
Tamaño: 3.606Mb
Formato: PDF
Descripción: Versión editorial
Abrir/Preview

Metadatos del ítem

Título: Diseño de una arquitectura para sistemas y aplicaciones en Industria 4.0 basada en computación en la nube y análisis de datos
Otro titulo: Design of an architecture for systems and applications in Industry 4.0 based on cloud computing and data analysis
Autor: Belman-López, Carlos E. Jiménez-García, José A. Vázquez-Lopez, José A. Camarillo-Gómez, Karla A.
Fecha difusión:
Resumen:
[EN] Industry 4.0 has become a priority and object of study for companies and research centers, although it is still in its early stages of implementation. In addition, companies face difficulties in developing solutions ...[+]


[ES] El término Industria 4.0 se ha convertido en prioridad y objeto de estudio para empresas y centros de investigación pero aún se encuentra dentro de sus primeras etapas de implementación. Además, las compañías enfrentan ...[+]
Palabras clave: Industry 4.0 , System architecture , Cloud computing , Data analysis , Applications development , Industria 4.0 , Arquitectura de sistemas , Computación en la nube , Análisis de datos , Desarrollo de aplicaciones
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.2022.17791
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/riai.2022.17791
Código del Proyecto:
info:eu-repo/grantAgreement/CONACYT//CVU 773443
Agradecimientos:
Los autores agradecen al Consejo Nacional de Ciencia y Tecnología de México (CONACYT) por financiar esta investigación mediante una beca para estudios de posgrado (CVU 773443), al TecNM por el apoyo recibido a través de ...[+]
Tipo: Artículo

References

Aheleroff, S., Xu, X., Zhong, R., & Lu, Y. (2021). Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model. Advanced Engineering Informatics, 1-15. https://doi.org/10.1016/j.aei.2020.101225

Almada-Lobo, F. (2015). The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES). Journal of Innovation Management, 16-21. https://doi.org/10.24840/2183-0606_003.004_0003

Amazon Web Services. (2022). Infrastructura Global. Obtenido de AWS: https://aws.amazon.com/es/about-aws/global-infrastructure/ [+]
Aheleroff, S., Xu, X., Zhong, R., & Lu, Y. (2021). Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model. Advanced Engineering Informatics, 1-15. https://doi.org/10.1016/j.aei.2020.101225

Almada-Lobo, F. (2015). The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES). Journal of Innovation Management, 16-21. https://doi.org/10.24840/2183-0606_003.004_0003

Amazon Web Services. (2022). Infrastructura Global. Obtenido de AWS: https://aws.amazon.com/es/about-aws/global-infrastructure/

Angulo, P., Guzmán, C., Jiménez, G., & Romero, D. (2016). A service-oriented architecture and its ICT infrastructure to support eco-efficiency performance monitoring in manufacturing enterprises. International Journal of Computer Integrated Manufacturing, 202-214. https://doi.org/10.1080/0951192X.2016.1145810

AWS. (2022). Regiones y zonas de disponibilidad. Obtenido de AWS: https://aws.amazon.com/es/about-aws/global-infrastructure/regions_az/

Azeem, M., Haleem, A., Shashi, B., Javaid, M., Suman, R., & Nandan, D. (2021). Big data applications to take up major challenges across manufacturing industries: A brief review. Materials Today: Proceedings, 1-10. https://doi.org/10.1016/j.matpr.2021.02.147

Bader, S., Berres, B., Boss, B., Gatterburg, A., & Hoffmeister, M. (Noviembre de 2021). Plattform Industrie 4.0. Obtenido de Details of the Asset Administration Shell - Interoperability at Runtime - Part 2: Exchanging Information via Application Programming Interfaces: https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/Details_of_the_Asset_Administration_Shell_Part2_V1.html

Bauer, J. (24 de Feb de 2021). Using container images to run TensorFlow models in AWS Lambda. Obtenido de AWS: https://aws.amazon.com/es/blogs/machine-learning/using-container-images-to-run-tensorflow-models-in-aws-lambda/

Belman-Lopez, 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, 432-447. https://doi.org/10.4995/riai.2020.12579

Belman-López, C., Jiménez-García, J., Vázquez-López, J., Hernández-González, S., & Franco-Barrón, J. (2020). Elementos fundamentales del sistema de manufactura inteligente en la era de Industria 4.0. Revista Internacional de Investigación e Innovación Tecnológica, 1-26.

Caggiano, A. (2018). Cloud-based manufacturing process monitoring for smart diagnosis services. International Journal of Computer Integrated Manufacturing, 31(7), 612-623. https://doi.org/10.1080/0951192X.2018.1425552

Carnell, J. (2017). Spring Microservices in Action. NY: Manning Publications Co.

Cervantes Maceda, H., Velasco-Elizondo, P., & Castro Careaga, L. (2016). Arquitectura de Software. Conceptos y ciclo de desarrollo. Ciudad de México, México: CENGAGE Learning.

Charro, A., & Schaefer, D. (2018). Cloud Manufacturing as a new type of Product-Service System. International Journal of Computer Integrated Manufacturing, 1018-1033. https://doi.org/10.1080/0951192X.2018.1493228

Chen, T., & Tsai, H.-R. (2016). Ubiquitous manufacturing: Current practices, challenges, and opportunities. Robotics and Computer-Integrated Manufacturing, 1-7. http://dx.doi.org/10.1016/j.rcim.2016.01.001

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, 300-311. https://doi.org/10.4995/riai.2021.14532

Docker. (2021). Obtenido de Docker: https://www.docker.com/

Francalanza, E., Borg, J., & Constantinescu, C. (2018). Approaches for handling wicked manufacturing system design problems. Procedia CIRP, 67, 134-139. https://doi.org/10.1016/j.procir.2017.12.189

GE. (01 de Noviembre de 2018). Predix Platform | GE Digital. Obtenido de GE: https://www.ge.com/digital/iiot-platform

Geest, M., Tekinerdogan, B., & Catal, C. (2021). Design of a reference architecture for developing smart warehouses in Industry 4.0. Computers in Industry, 1-21. https://doi.org/10.1016/j.compind.2020.103343

Ghobakhloo, M. (2018). The future of manufacturing industry: a strategic road0toward Industry 4.0. Journal of Manufacturing Technology Management, 910-936. https://doi.org/10.1108/JMTM-02-2018-0057

Google Cloud. (2018). Web API Design: The Missing Link. Google LLC.

Gorton, I., & Klein, J. (2015). Distribution, Data, Deployment, Software Architecture Convergence in Big Data Systems. IEEE COMPUTER SOCIETY, 78-85. https://doi.org/10.1109/MS.2014.51

Groover, M. (2001). Automation, Production Systems, and Computer-Integrated Manufacturing. USA: Prentice Hall.

Hermann, M., Otto, B., & Pentek, T. (2015). Design Principles for Industrie 4.0 Scenarios: A Literature Review. ResearchGate, 1-16. https://doi.org/10.1109/HICSS.2016.488

Hohpe, G., & Woolf, B. (2004). Enterprise Integration Patterns. Boston, MA: Addison-Wesley.

Huang , M.-L., & Chuang, T. (2020). A database of eight common tomato pest images. Mendeley Data, V1. doi:10.17632/s62zm6djd2.1

ISA. (Octubre de 2019). Automation IT: RAMI 4.0 Reference Architectural Model for Industrie 4.0. Obtenido de International Society of Automation (ISA): https://www.isa.org/intech/20190405/

ISO/IEC/IEEE 42010. (10 de Jul de 2007). ISO/IEC/IEEE 42010: Defining "architecture". Obtenido de ISO/IEC/IEEE 42010: http://www.iso-architecture.org/ieee-1471/defining-architecture.html

Jocher, G., Stoken, A., Chaurasia, A., Borovec, J., NanoCode012, TaoXie, . . . AlexWang1900. (2021). ultralytics/yolov5: v6.0. Zenodo. https://doi.org/10.5281/zenodo.5563715

Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 36-52. https://doi.org/10.1016/j.cirpj.2020.02.002

Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final report of the Industrie 4.0 Working Group. National Academy of Science and Engineering (acatech)., 1-82. https://doi.org/10.3390/sci4030026

Kakani, V., Nguyen, V., Kumar, B., Kim, H., & Pasupuleti, V. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 1-12. https://doi.org/10.1016/j.jafr.2020.100033

Karatas, M., Eriskin, L., Deveci, M., Pamucar, D., & Garg, H. (2022). Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives. Expert Systems with Applications, 1-13. https://doi.org/10.1016/j.eswa.2022.116912

Kusiak, A. (2017). Smart manufacturing. International Journal of Production Research, 508-517. https://doi.org/10.1080/00207543.2017.1351644

Lee, J., Ardakani, H., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP, 3-7. https://doi.org/10.1016/j.procir.2015.08.026

Lie, J., & Wang, X. (2021). Plant diseases and pests detection based on deep learning: a review. Plant Methods, 1-18. https://doi.org/10.1186/s13007-021-00722-9

Liu, C., Vengayil, H., Lu, Y., & Xu, X. (2019). A Cyber-Physical Machine Tools Platform using OPC UA and MTConnect. Journal of Manufacturing Systems, 1-14. https://doi.org/10.1016/j.jmsy.2019.04.006

Liu, Y., Peng, Y., Wang, B., Yao, S., & Liu, Z. (2017). Review on Cyber-physical Systems. Journal of Automatica Sinica, 27-40. https://doi.org/10.1109/JAS.2017.7510349

Liu, Z., Sampaio, P., Pishchulov, G., Mehandjiev, N., Cisneros-Cabrera, S., Schirrmann, A., . . . Bnouhanna, N. (2022). The architectural design and implementation of a digital platform for Industry 4.0 SME collaboration. Computers in Industry, 1-12. https://doi.org/10.1016/j.compind.2022.103623

López Martínez, P., Dintén, R., Drake, J., & Zorrilla, M. (2021). A big data-centric architecture metamodel for Industry 4.0. Future Generation Computer Systems, 263-284. https://doi.org/10.1016/j.future.2021.06.020

Lu, Y., Liu, C., Wang, K.-K., Huang, H., & Xu, X. (2019). Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer Integrated Manufacturing, 1-14. https://doi.org/10.1016/j.rcim.2019.101837

Macías, A., Navarro, E., & González, P. (2019). A Microservice-Based Framework for Developing Internet of Things and People Applications. Proceedings, 1-13. https://doi.org/10.3390/proceedings2019031085

Malathi, V., & Gopinath, M. (2021). Classification of pest detection in paddy crop based on transfer learning approach. Acta Agriculturae Scandinavica, Section B - Soil & Plant Science. https://doi.org/10.1080/09064710.2021.1874045

Miny, T., Stephan, G., Usländer, T., & Vialkowitsch, J. (Abril de 2021). Plattform Industrie 4.0. Obtenido de Functional View of the Asset Administration Shell in an Industrie 4.0 System Environment: https://www.plattform-i40.de/IP/Redaktion/DE/Downloads/Publikation/Functional-View.html

Mishra, A. (2019). Machine Learning in the AWS Cloud. Indianapolis, Indiana: John Wiley & Sons, Inc. https://doi.org/10.1002/9781119556749

Nakagawa, E. Y., Antonino, P. O., Schnicke, F., Capilla, R., Kuhn, T., & Liggesmeyer, P. (2021). Industry 4.0 reference architectures: State of the art and future trends. Computers & Industrial Engineering, 1-13. https://doi.org/10.1016/j.cie.2021.107241

Niknejad, N., Ismail, W., Ghani, I., Nazari, B., Bahari, M., & Hussin, A. (2020). Understanding Service-Oriented Architecture (SOA): A systematic literature review and directions for further investigation. Information Systems, 1-27. https://doi.org/10.1016/j.is.2020.101491

NIST. (16 de Abril de 2018). Framework for Improving Critical Infrastructure Cybersecurity. Obtenido de National Institute of Standards and Technology: https://nvlpubs.nist.gov/nistpubs/CSWP/NIST.CSWP.04162018.pdf

Pallathadka, H., Sajja, G., Phasinam, K., Ritonga, M., Naved, M., Bansal, R., & Quiñonez-Choquecota, J. (2021). An investigation of various applications and related challenges in cloud computing. Materials Today: Proceedings, 1-5. https://doi.org/10.1016/j.matpr.2021.11.383

Pereira, A., & Romero, F. (2017). A review of the meaning and the implications of the Industry 4.0 concept. En P. Manufacturing (Ed.), Manufacturing Engineering Society International Conference (págs. 1206-1214). Vigo, España: Elsevier. https://doi.org/10.1016/j.promfg.2017.09.032

Poccia, D. (2016). AWS Lambda in Action. Manning.

PwC Middle East. (23 de 10 de 2018). Big investments with big impacts and rapid returns. Obtenido de PwC Middle East : https://www.pwc.com/m1/en/publications/industry-40-survey/big-investments.html

Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., . . . Nee, A. (2019). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 1-19. https://doi.org/10.1016/j.jmsy.2019.10.001

R, S., & R, S. (2017). Data Mining with Big Data. Intelligent Systems and Control (ISCO) (págs. 246-250). Coimbatore, India: IEEE. doi: 10.1109/ISCO.2017.7855990

RedHat. (2021). ¿Que es una api rest? Obtenido de RedHat: https://www.redhat.com/es/topics/api/what-is-a-rest-api#rest

Richards, M. (2015). Software Arquitecture Patterns. Gravenstein Highway North, Sebastopol, CA: O'Reilly Media, Inc.

Rosen, D. (2019). Thoughts on Design for Intelligent Manufacturing. Engineering, 1-6. https://doi.org/10.1016/j.eng.2019.07.011

Sahba, R., Radfar, R., Ghatari, A. R., & Ebrahimi, A. P. (2021). Development of Industry 4.0 predictive maintenance architecture for broadcasting chain. Advanced Engineering Informatics, 1-11. https://doi.org/10.1016/j.aei.2021.101324

Singh, D., Jain, N., Jain, P., & Kayal, P. (2019). PlantDoc: A Dataset for Visual Plant Disease Detection. arXivLabs, 1-5. https://doi.org/10.1145/3371158.3371196

Software Engineering Institute. (04 de May de 2018). Attribute-Driven Design - Create software architectures using architecturally significant requirements. Obtenido de Software Engineering Institute at Carnegie Mellon University: https://resources.sei.cmu.edu/asset_files/FactSheet/2018_010_001_513930.pdf

Sony, M., Antony, J., Mc Dermott, O., & Garza-Reyes, J. (2021). An empirical examination of benefits, challenges, and critical success factors of industry 4.0 in manufacturing and service sector. Technology in Society, 1-12. https://doi.org/10.1016/j.techsoc.2021.101754

Tao, F., Qi, Q., Wang, L., & Nee, A. (2019). Digital Twins and Cyber-Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. Engineering, 653-661. https://doi.org/10.1016/j.eng.2019.01.014

The Apache Software Foundation. (2020). Apache Avro. Obtenido de Apache Avro: https://avro.apache.org/

Tian, W., & Zhao, Y. (2015). Optimized Cloud Resource Management and Scheduling. Morgan Kaufmann. :https://doi.org/10.1016/C2013-0-13415-0

Tuptuk, N., & Hailes, S. (2018). Security of smart manufacturing systems. Journal of Manufacturing Systems, 93-106. https://doi.org/10.1016/j.jmsy.2018.04.007

Wang, X., Ong, S., & Nee, A. (2017). A comprehensive survey of ubiquitous manufacturing research. International Journal of Production Research, 604-628. https://doi.org/10.1080/00207543.2017.1413259

Wankhede, V. A., & Vinodh, S. (2021). Analysis of Industry 4.0 challenges using best worst method: A case study. Computers & Industrial Engineering, 1-13. https://doi.org/10.1016/j.cie.2021.107487

Wiesner, S., & Thoben, K.-D. (2016). Requirements for models, methods and tools supporting servitisation of products in manufacturing service ecosystems. International Journal of Computer Integrated Manufacturing, 1-12. https://doi.org/10.1080/0951192X.2015.1130243

Xu, L. D., & Duan, L. (2018). Big data for cyber physical systems in industry 4.0: a survey. Enterprise Information Systems, 1-23. https://doi.org/10.1080/17517575.2018.1442934

Xu, L., Xu, E., & Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56, 2941-2962. https://doi.org/10.1080/00207543.2018.1444806

Yang, H., Kumara, S., Bukkapatnam, S., & Tsung, F. (2019). The Internet of Things for Smart Manufacturing: A Review. IISE Transactions, 1-36. https://doi.org/10.1080/24725854.2018.1555383

Zhong, R., Xu, X., Klotz, E., & Newman, S. (2017). Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, 616-630. https://doi.org/10.1016/J.ENG.2017.05.015

[-]

recommendations

 

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

Mostrar el registro completo del ítem