Skip to main content

Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors

  • Conference paper
  • First Online:
Applied Computer Sciences in Engineering (WEA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1052))

Included in the following conference series:

Abstract

Industrial Internet of Things has become a reality in many kind of industries. In this paper, We explore the case of high quantity of raw data generated by a machine. In the aforementioned case is not viable store and process the data in a traditional Internet of Things architecture. For this case, We use an architecture based on edge computing and Industrial Internet of Things concepts and apply them to a case of machine monitoring for predictive maintenance. The proof of concept shows the potential benefits in real industrial applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Gregori, F., Papetti, A., Pandolfi, M., Peruzzini, M., Germani, M.: Improving a production site from a social point of view: an IoT infrastructure to monitor workers condition. Procedia CIRP 72, 886–891 (2018). https://doi.org/10.1016/j.procir.2018.03.057. http://www.sciencedirect.com/science/article/pii/S2212827118301598. ISSN2212-8271

    Article  Google Scholar 

  2. Edge computing consortium. White paper of edge computing consortium (2016)

    Google Scholar 

  3. Boyes, H., Hallaq, B., Cunningham, J., Watson, T.: The industrial Internet of Things (IIoT): an analysis framework. Comput. Ind. 101, 1–12 (2018). https://doi.org/10.1016/j.compind.2018.04.015. http://www.sciencedirect.com/science/article/pii/S0166361517307285. ISSN 0166-3615

    Article  Google Scholar 

  4. Civerchia, F., Bocchino, S., Salvadori, C., Rossi, E., Maggiani, L., Petracca, M.: Industrial Internet of Things monitoring solution for advanced predictive maintenance applications. J. Ind. Inf. Integr. 7, 4–12 (2017). https://doi.org/10.1016/j.jii.2017.02.003. http://www.sciencedirect.com/science/article/pii/S2452414X16300954. ISSN 2452-414X

    Article  Google Scholar 

  5. Quinn, J.: The real goal of maintenance engineering, in factory. In: Collins, A.W. (ed.) The Measurement of Naval Facilities Maintenance Effectiveness. Naval Postgraduate School, Monterey CA, p. 90-3 (1964)

    Google Scholar 

  6. Cao, J., Zhang, Q., Li, Y., Shi, W., Xu, L.: Edge computing: vision and challenges. IEEE IoT J. 3(16286981), 637–646 (2016)

    Google Scholar 

  7. Industrial Internet Consortium. Introduction to edge computing in IIoT. An Industrial Internet Consortium White Paper, IIC:WHT:IN24:V1.0:PB:20180618. Edge Computing Task Group

    Google Scholar 

  8. Schmidt, B., Wang, L., Galar, D.: Semantic framework for predictive maintenance in a cloud environment. Procedia CIRP 62, 583–588 (2017). https://doi.org/10.1016/j.procir.2016.06.047. ISSN 2212-8271

    Article  Google Scholar 

  9. Taherizadeh, S., Jones, A.C., Taylor, I., Zhao, Z., Stankovski, V.: Monitoring self-adaptive applications within edge computing frameworks: a state-of-the-art review. J. Syst. Softw. 136(Suppl. C), 19–38 (2018)

    Article  Google Scholar 

  10. Fujishima, M., Mori, M., Nishimura, K., Takayama, M., Kato, Y.: Development of sensing interface for preventive maintenance of machine tools. Procedia CIRP 61, 796–799 (2017). https://doi.org/10.1016/j.procir.2016.11.206. http://www.sciencedirect.com/science/article/pii/S2212827116313749. ISSN 2212-8271

    Article  Google Scholar 

  11. Cruz, A.M.E.: ESTUDIO DE UN SISTEMA DE MANTENIMIENTO PREDICTIVO BASADO EN ANÁLISIS DE VIBRACIONES IMPLANTADO EN INSTALACIONES DE BOMBEO Y GENERACIÓN (2013)

    Google Scholar 

  12. Power-MI, Manual Análisis de Vibraciones. https://power-mi.com/es/content/power-mi-lanza-manual-de-an

  13. Pease, S.G., Conway, P.P., West, A.A.: Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture. J. Netw. Comput. Appl. 99, 98–109 (2017)

    Article  Google Scholar 

  14. Flores, R., Asiaín, T.I.: Diagnóstico de Fallas en Máquinas Eléctricas Rotatorias Utilizando la Técnica de Espectros de Frecuencia de Bandas Laterales. Información Tecnológica 22(4), 73–84 (2011). https://doi.org/10.4067/S0718-07642011000400009

    Article  Google Scholar 

  15. Talbot, C.E., Saavedra, P.N., Valenzuela, M.A.: Diagnóstico de la Condición de las Barras de Motores de Inducción. Información tecnológica 24(4), 85–94 (2013). https://doi.org/10.4067/S0718-07642013000400010

    Article  Google Scholar 

  16. Lin, S.-W.: Architecture alignment and interoperability (2017)

    Google Scholar 

  17. Mourtzis, D., Gargallis, A., Zogopoulos, V.: Modelling of customer oriented applications in product lifecycle using RAMI 4.0. Procedia Manuf. 28, 31–36 (2019). https://doi.org/10.1016/j.promfg.2018.12.006. http://www.sciencedirect.com/science/article/pii/S2351978918313489. ISSN 2351-9789

    Article  Google Scholar 

  18. Lin, S.W., et al.: Industrial internet reference architecture. Technical report, Industrial Internet Consortium (IIC) (2015)

    Google Scholar 

  19. Packard, H.: Real-time analysis and condition monitoring with predictive maintenance. Transforming data into value with HPE Edgeline (2017)

    Google Scholar 

  20. Gierej, S.: The framework of business model in the context of industrial Internet of Things. Procedia Eng. 182, 206–212 (2017). https://doi.org/10.1016/j.proeng.2017.03.166. http://www.sciencedirect.com/science/article/pii/S1877705817313024. ISSN 1877-7058

    Article  Google Scholar 

  21. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE IoT J. 3(5), 637–646 (2016)

    Google Scholar 

  22. Barroso, M., Dolores, M.: Edge computing para IoT (2019)

    Google Scholar 

  23. Bossio, G., De Angelo, C., García, G.: Técnicas de Mantenimiento Predictivo en Máquinas Eléctricas: Diagnóstico de Fallas en el Rotor de los Motores de Inducción. Megavatios, pp. 194–208 (2006)

    Google Scholar 

  24. Bellini, A., et al.: On-field experience with online diagnosis of large induction motors cage failures using MCSA. IEEE Trans. Ind. Appl. 38(4), 1045–1053 (2002). https://doi.org/10.1109/TIA.2002.800591

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like acknowledge the cooperation of all partners within the Centro de Excelencia y Apropiación en Internet de las Cosas (CEA-IoT) project. The authors would also like to thank all the institutions that supported this work: the Colombian Ministry for the Information and Communications Technology (Ministerio de Tecnologías de la Información y las Comunicaciones - MinTIC) and the Colombian Administrative Department of Science, Technology and Innovation (Departamento Administrativo de Ciencia, Tecnología e Innovación - Colciencias) through the Fondo Nacional de Financiamiento para la Ciencia, la Tecnología y la Innovación Francisco José de Caldas (Project ID: FP44842-502-2015).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jose Luis Villa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

De Leon, V., Alcazar, Y., Villa, J.L. (2019). Use of Edge Computing for Predictive Maintenance of Industrial Electric Motors. In: Figueroa-García, J., Duarte-González, M., Jaramillo-Isaza, S., Orjuela-Cañon, A., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2019. Communications in Computer and Information Science, vol 1052. Springer, Cham. https://doi.org/10.1007/978-3-030-31019-6_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31019-6_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31018-9

  • Online ISBN: 978-3-030-31019-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics