Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
Alsyouf, I.: The role of maintenance in improving companies’ productivity and profitability. Int. J. Prod. Econ. 105(1), 70–78 (2007)
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)
Colledani, M., et al.: Design and management of manufacturing systems for production quality. CIRP Ann. 63(2), 773–796 (2014)
Efthymiou, K., Pagoropoulos, A., Papakostas, N., Mourtzis, D., Chryssolouris, G.: Manufacturing systems complexity review: challenges and outlook. Procedia CIRP 3, 644–649 (2012)
Efthymiou, K., Papakostas, N., Mourtzis, D., Chryssolouris, G.: On a predictive maintenance platform for production systems. Procedia CIRP 3, 221–226 (2012)
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)
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer Series in Statistics, vol. 1. Springer, New York (2001). https://doi.org/10.1007/978-0-387-84858-7
Gupta, M.: Fog computing pushing intelligence to the edge. Int. J. Sci. Technol. Eng. 3(8), 4246 (2017)
Lee, E.: The past, present and future of cyber-physical systems: a focus on models. Sensors 15(3), 4837–4869 (2015)
Li, S., Maddah-Ali, M.A., Avestimehr, A.S.: Coding for distributed fog computing. IEEE Commun. Mag. 55(4), 34–40 (2017)
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)
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)
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). https://doi.org/10.1007/978-981-10-5861-5_5
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)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson Education Limited, Malaysia (2016)
Schmidt, B., Wang, L., Galar, D.: Semantic framework for predictive maintenance in a cloud environment. Procedia CIRP 62, 583–588 (2017)
Shin, J.H., Jun, H.B.: On condition based maintenance policy. J. Comput. Des. Eng. 2(2), 119–127 (2015)
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)
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)
Van Horenbeek, A., Pintelon, L.: A dynamic predictive maintenance policy for complex multi-component systems. Reliab. Eng. Syst. Saf. 120, 39–50 (2013)
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)
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)
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cerquitelli, T. et al. (2019). A Fog Computing Approach for Predictive Maintenance. In: Proper, H., Stirna, J. (eds) Advanced Information Systems Engineering Workshops. CAiSE 2019. Lecture Notes in Business Information Processing, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-20948-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-20948-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20947-6
Online ISBN: 978-3-030-20948-3
eBook Packages: Computer ScienceComputer Science (R0)