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An Agent-Based Industrial Cyber-Physical System Deployed in an Automobile Multi-stage Production System

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Abstract

Industrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitivity. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches such as those based on Multi-agent Systems (MAS) are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers.

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References

  1. Aazam, M., Zeadally, S., Harras, K.A.: Deploying fog computing in industrial Internet of Things and industry 4.0. IEEE Trans. Industr. Inf. 14(10), 4674–4682 (2018)

    Article  Google Scholar 

  2. Breivold, H., Sandström, K.: Internet of Things for industrial automation – challenges and technical solutions. In: 2015 IEEE International Conference on Data Science and Data Intensive Systems, pp. 532–539 (2015)

    Google Scholar 

  3. Calvaresi, D., Appoggetti, K., Lustrissimi, L., Marinoni, M., Sernani, P., Dragoni, A.F., Schumacher, M.: Multi-agent systems’ negotiation protocols for cyber-physical systems: results from a systematic literature review. In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1, ICAART, pp. 224–235. SciTePress (2018)

    Google Scholar 

  4. Chiang, M., Zhang, T.: Fog and IoT: an overview of research opportunities. IEEE Internet of Things J. 3(6), 854–864 (2016)

    Article  Google Scholar 

  5. Demšar, J., Bosnić, Z.: Detecting concept drift in data streams using model explanation. Expert Syst. Appl. 92, 546–559 (2018)

    Article  Google Scholar 

  6. Domingues, R., Filippone, M., Michiardi, P., Zouaoui, J.: A comparative evaluation of outlier detection algorithms: experiments and analyses. Pattern Recogn. 74, 406–421 (2018)

    Article  Google Scholar 

  7. Fei, X., Shah, N., Verba, N., Chao, K.M., Sanchez-Anguix, V., Lewandowski, J., James, A., Usman, Z.: CPS data streams analytics based on machine learning for cloud and fog computing: a survey. Future Gener. Comput. Syst. 90, 435–450 (2019)

    Article  Google Scholar 

  8. Lee, J., Davari, H., Singh, J., Pandhare, V.: Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manufact. Lett. 18, 20–23 (2018)

    Article  Google Scholar 

  9. Leitão, P., Barbosa, J., Geraldes, C.A.S., Coelho, J.P.: Multi-agent system architecture for zero defect multi-stage manufacturing, pp. 13–26. Springer, Cham (2018)

    Chapter  Google Scholar 

  10. Leitão, P., Karnouskos, S., Ribeiro, L., Lee, J., Strasser, T., Colombo, A.W.: Smart agents in industrial cyber-physical systems. Proc. IEEE 104(5), 1086–1101 (2016)

    Article  Google Scholar 

  11. Li, L., Ota, K., Dong, M.: Deep learning for smart industry: efficient manufacture inspection system with fog computing. IEEE Trans. Industr. Inf. 14(10), 4665–4673 (2018)

    Article  Google Scholar 

  12. Lu, Y.: Industry 4.0: a survey on technologies, applications and open research issues. J. Industr. Inf. Integr. 6, 1–10 (2017)

    Google Scholar 

  13. Peres, R., Rocha, A., Matos, J., Barata, J.: Go0dman data model - interoperability in multistage zero defect manufacturing. In: IEEE 16th International Conference on Industrial Informatics (INDIN) (2018)

    Google Scholar 

  14. Pico-Valencia, P., Holgado-Terriza, J.A.: Agentification of the Internet of Things: a systematic literature review. Int. J. Distrib. Sens. Netw. 14(10), 1–20 (2018)

    Article  Google Scholar 

  15. Ren, L., Zhang, L., Wang, L., Tao, F., Chai, X.: Cloud manufacturing: key characteristics and applications. Int. J. Comput. Integr. Manufact. 30(6), 501–515 (2017)

    Article  Google Scholar 

  16. Teerapittayanon, S., McDanel, B., Kung, H.: Distributed deep neural networks over the cloud, the edge and end devices. In: Proceedings of 37th IEEE International Conference on Distributed Computing Systems, pp. 328–339 (2017)

    Google Scholar 

  17. Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing: methods and applications. J. Manufact. Syst. 48, 144–156 (2018). Special Issue on Smart Manufacturing

    Article  Google Scholar 

  18. Wang, S., Wan, J., Zhang, D., Li, D., Zhang, C.: Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput. Netw. 101, 158–168 (2016)

    Article  Google Scholar 

  19. Wu, D., Liu, S., Zhang, L., Terpenny, J., Gao, R.X., Kurfess, T., Guzzo, J.A.: A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. J. Manufact. Syst. 43, 25–34 (2017)

    Article  Google Scholar 

  20. Wuest, T., Weimer, D., Irgens, C., Thoben, K.D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manufact. Res. 4(1), 23–45 (2016)

    Article  Google Scholar 

  21. Xiao, Z., Xiao, Y.: Security and privacy in cloud computing. IEEE Commun. Surv. Tutor. 15(2), 843–859 (2013)

    Article  Google Scholar 

  22. Xu, H., Yu, W., Griffith, D., Golmie, N.: A survey on industrial Internet of Things: a cyber-physical systems perspective. IEEE Access 6, 78238–78259 (2018)

    Article  Google Scholar 

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Acknowledgements

This work is part of the GO0D MAN project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 723764.

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Correspondence to Jonas Queiroz .

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Queiroz, J., Leitão, P., Barbosa, J., Oliveira, E., Garcia, G. (2020). An Agent-Based Industrial Cyber-Physical System Deployed in an Automobile Multi-stage Production System. In: Borangiu, T., Trentesaux, D., Leitão, P., Giret Boggino, A., Botti, V. (eds) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2019. Studies in Computational Intelligence, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-030-27477-1_29

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