A Fog Computing Approach for Predictive Maintenance

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 349)


Technological advances in areas such as communications, computer processing, connectivity, data management are gradually introducing the internet of things (IoT) paradigm across companies of different domain. In this context and as systems are making a shift into cyber-physical system of systems, connected devices provide massive data, that are usually streamed to a central node for further processing. In particular and related to the manufacturing domain, Data processing can provide insight in the operational condition of the organization or process monitored. However, there are near real time constraints for such insights to be generated and data-driven decision making to be enabled. In the context of internet of things for smart manufacturing and empowered by the aforementioned, this study discusses a fog computing paradigm for enabling maintenance related predictive analytic in a manufacturing environment through a two step approach: (1) Model training on the cloud, (2) Model execution on the edge. The proposed approach has been applied to a use case coming from the robotic industry.


Internet of things Predictive analytics Cyber-physical system 



The research leading to these results has received funding from European Commission under the H2020-IND-CE-2016-17 program, FOF-09-2017, Grant agreement no. 767561 “SERENA” project, VerSatilE plug-and-play platform enabling REmote predictive mainteNAnce.


  1. 1.
    Alrawais, A., Alhothaily, A., Hu, C., Cheng, X.: Fog computing for the internet of things: security and privacy issues. IEEE Internet Comput. 21(2), 34–42 (2017)CrossRefGoogle Scholar
  2. 2.
    Alsyouf, I.: The role of maintenance in improving companies’ productivity and profitability. Int. J. Prod. Econ. 105(1), 70–78 (2007)CrossRefGoogle Scholar
  3. 3.
    Anawar, M.R., Wang, S., Azam Zia, M., Jadoon, A.K., Akram, U., Raza, S.: Fog computing: an overview of big IoT data analytics. Wirel. Commun. Mob. Comput. 2018 (2018)Google Scholar
  4. 4.
    Colledani, M., et al.: Design and management of manufacturing systems for production quality. CIRP Ann. 63(2), 773–796 (2014)CrossRefGoogle Scholar
  5. 5.
    Efthymiou, K., Pagoropoulos, A., Papakostas, N., Mourtzis, D., Chryssolouris, G.: Manufacturing systems complexity review: challenges and outlook. Procedia CIRP 3, 644–649 (2012)CrossRefGoogle Scholar
  6. 6.
    Efthymiou, K., Papakostas, N., Mourtzis, D., Chryssolouris, G.: On a predictive maintenance platform for production systems. Procedia CIRP 3, 221–226 (2012)CrossRefGoogle Scholar
  7. 7.
    Fisher, O., Watson, N., Porcu, L., Bacon, D., Rigley, M., Gomes, R.L.: Cloud manufacturing as a sustainable process manufacturing route. J. Manuf. Syst. 47, 53–68 (2018)CrossRefGoogle Scholar
  8. 8.
    Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer Series in Statistics, vol. 1. Springer, New York (2001). Scholar
  9. 9.
    Gupta, M.: Fog computing pushing intelligence to the edge. Int. J. Sci. Technol. Eng. 3(8), 4246 (2017)Google Scholar
  10. 10.
    Lee, E.: The past, present and future of cyber-physical systems: a focus on models. Sensors 15(3), 4837–4869 (2015)CrossRefGoogle Scholar
  11. 11.
    Li, S., Maddah-Ali, M.A., Avestimehr, A.S.: Coding for distributed fog computing. IEEE Commun. Mag. 55(4), 34–40 (2017)CrossRefGoogle Scholar
  12. 12.
    Lindström, J., Larsson, H., Jonsson, M., Lejon, E.: Towards intelligent and sustainable production: combining and integrating online predictive maintenance and continuous quality control. Procedia CIRP 63, 443–448 (2017)CrossRefGoogle Scholar
  13. 13.
    Lu, C.W., Hsieh, C.M., Chang, C.H., Yang, C.T.: An improvement to data service in cloud computing with content sensitive transaction analysis and adaptation. In: 2013 IEEE 37th Annual Computer Software and Applications Conference Workshops, pp. 463–468. IEEE (2013)Google Scholar
  14. 14.
    Mahmud, R., Kotagiri, R., Buyya, R.: Fog computing: a taxonomy, survey and future directions. In: Di Martino, B., Li, K.-C., Yang, L.T., Esposito, A. (eds.) Internet of Everything. IT, pp. 103–130. Springer, Singapore (2018). Scholar
  15. 15.
    Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R.H., Morrow, M.J., Polakos, P.A.: A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun. Surv. Tutor. 20(1), 416–464 (2018)CrossRefGoogle Scholar
  16. 16.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson Education Limited, Malaysia (2016)zbMATHGoogle Scholar
  17. 17.
    Schmidt, B., Wang, L., Galar, D.: Semantic framework for predictive maintenance in a cloud environment. Procedia CIRP 62, 583–588 (2017)CrossRefGoogle Scholar
  18. 18.
    Shin, J.H., Jun, H.B.: On condition based maintenance policy. J. Comput. Des. Eng. 2(2), 119–127 (2015)Google Scholar
  19. 19.
    Spendla, L., Kebisek, M., Tanuska, P., Hrcka, L.: Concept of predictive maintenance of production systems in accordance with industry 4.0. In: 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000405–000410. IEEE (2017)Google Scholar
  20. 20.
    Tsang, A.H., Yeung, W., Jardine, A.K., Leung, B.P.: Data management for cbm optimization. J. Qual. Maint. Eng. 12(1), 37–51 (2006)CrossRefGoogle Scholar
  21. 21.
    Van Horenbeek, A., Pintelon, L.: A dynamic predictive maintenance policy for complex multi-component systems. Reliab. Eng. Syst. Saf. 120, 39–50 (2013)CrossRefGoogle Scholar
  22. 22.
    Wang, S., Liu, Z., Sun, Q., Zou, H., Yang, F.: Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing. J. Intell. Manuf. 25(2), 283–291 (2014)CrossRefGoogle Scholar
  23. 23.
    Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5), 616–630 (2017)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Control and Computer EngineeringPolitecnico di TorinoTurinItaly
  2. 2.DELL EMCCorkIreland
  3. 3.Engineering Ingegneria Informatica S.p.A.PalermoItaly
  4. 4.COMAU S.p.A.TurinItaly
  5. 5.Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and AeronauticsUniversity of PatrasPatrasGreece
  6. 6.SynArea Consultants S.r.l.TurinItaly
  7. 7.Interuniversity Department of Regional and Urban Studies and PlanningPolitecnico di TorinoTurinItaly
  8. 8.Fraunhofer Gesellschaft zur Förderung der angewandten ForschungAachenGermany

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