A Fog Computing Approach for Predictive Maintenance
- 746 Downloads
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.
KeywordsInternet 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.
- 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
- 9.Gupta, M.: Fog computing pushing intelligence to the edge. Int. J. Sci. Technol. Eng. 3(8), 4246 (2017)Google Scholar
- 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
- 18.Shin, J.H., Jun, H.B.: On condition based maintenance policy. J. Comput. Des. Eng. 2(2), 119–127 (2015)Google Scholar
- 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