Skip to main content
SpringerLink
Log in

Low-Cost Fault Prediction System for a Rolling System on an Augmented Reality Platform with Cloud Communication

  • Conference paper
  • First Online:
Intelligent Technologies: Design and Applications for Society (CITIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 607))

  • 263 Accesses

Abstract

The application of Augmented Reality (AR) technologies within the industry has allowed for improving preventive and predictive maintenance techniques, thanks to the fact that they allow information to be presented in real time and in the same analyzed environment, which ensures that maintenance operational processes improve. This work presents the development of a predictive system for thermal stress failures, by applying the analysis of the Weibull statistical model and the use of free AR tools and cloud technology, which allow determining the reliability and average lifetime of induction coils in real time of a rolling system. The temperature data were obtained through a wireless sensor network (WSN) that sends the data to an embedded system (Raspberry Pi 4), which behaves as the communication channel (Gateway) between the sensors and the cloud, through the MQTT server. The results are presented in graphs and are promised under the Weibull model.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

References

  1. Contreras Alvarez, J.L.: DISEÑO DE UN MODELO PARA MANTENIMIENTO PREDICTIVO EN MOTORES DE INDUCCIÓN UTILIZANDO TÉCNICAS DE LA INDUSTRIA 4.0. Universidad Tecnológica del Perú, Lima (2020)

    Google Scholar 

  2. Johal, H., Mousavi, M.J.: Coil current analysis method for predictive maintenance of circuit breakers (2008). https://doi.org/10.1109/TDC.2008.4517278

  3. Li, L., Guan, J., Yuan, P., Yin, Y., Li, Y.: A Weibull distribution-based method for the analysis of concrete fracture. Eng. Fract. Mech. 256, 107964 (2021). https://doi.org/10.1016/J.ENGFRACMECH.2021.107964. Oct.

    Article  Google Scholar 

  4. Méndez-González, L.C., Rodríguez-Picón, L.A., Pérez Olguin, I.J.C., Garcia, V., Quezada-Carreón, A.E.: Reliability analysis for DC motors under voltage step-stress scenario. Electr. Eng. 102(3), 1433–1440 (2020). https://doi.org/10.1007/s00202-020-00966-z

    Article  Google Scholar 

  5. Salazar, R., Fitz, E., López, I., Rojano, A.: Confiabilidad y Análisis de Fallas utilizando la Distribución Weibull. Congr. Int. Investig. EN CIENCIAS BÁSICAS, 5–10 (2017). [Online]. Available: https://dicea.chapingo.mx/wp-content/uploads/2018/05/MEMORIA_MESA_1A_CONGRESO2017.pdf

  6. Krishna, G., et al.: Design and development of bi-directional IoT gateway using ZigBee and Wi-Fi technologies with MQTT protocol. Artic. Int. J. Eng. Technol. 7(2), 125–129 (2018). https://doi.org/10.14419/ijet.v7i2.8.10344

    Article  Google Scholar 

  7. Kashyap, M., Sharma, V., Gupta, N.: Taking MQTT and NodeMcu to IOT: Communication in Internet of Things. Procedia Comput. Sci. 132, 1611–1618 (2018). https://doi.org/10.1016/J.PROCS.2018.05.126. Jan.

    Article  Google Scholar 

  8. Upadhyay, Y., Borole, A., Dileepan, D.: MQTT based secured home automation system. In: 2016 Symp. Colossal Data Anal. Networking, CDAN 2016 (Sep. 2016). https://doi.org/10.1109/CDAN.2016.7570945

  9. Fernández del Amo, I., Erkoyuncu, J.A., Roy, R., Palmarini, R., Onoufriou, D.: A systematic review of Augmented Reality content-related techniques for knowledge transfer in maintenance applications. Comput. Ind. 103, 47–71 (2018). https://doi.org/10.1016/J.COMPIND.2018.08.007. Dec.

    Article  Google Scholar 

  10. Kollatsch, C., Schumann, M., Klimant, P., Lorenz, M.: Industrial Augmented Reality: Transferring a Numerical Control Connected Augmented Realty System from Marketing to Maintenance. In: Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017, pp. 39–41 (Oct. 2017). https://doi.org/10.1109/ISMAR-Adjunct.2017.27

  11. Feedback Instruments Ltd: Ball and Plate Control Experiments, vol. 33–240-Lab, no. 1160 (2008)

    Google Scholar 

  12. Núñez, H.: Development and implementation of a fuzzy controller using machine vision feedback applied to a ball-and-plate positioning system, p. 86 (2017)

    Google Scholar 

  13. Qiu, H., Zhao, X., Yang, C., Ran, Y., Wei, Y.: Influence of inter-turn short-circuit fault considering loop current on electromagnetic field of high-speed permanent magnet generator with gramme ring windings. Journal of Electrical Engineering & Technology 14(2), 701–710 (2019). https://doi.org/10.1007/s42835-019-00122-z

    Article  Google Scholar 

  14. Benchabane, F., Guettaf, A., Yahia, K., Sahraoui, M.: Experimental investigation on induction motors inter-turns short-circuit and broken rotor bars faults diagnosis through the discrete wavelet transform. e i Elektrotechnik und Informationstechnik 20181352 135(2), 187–194 (2018). https://doi.org/10.1007/s00502-018-0607-6. Mar.

    Article  Google Scholar 

  15. Griggs, J.A., Zhang, Y.: Determining the confidence intervals of Weibull parameters estimated using a more precise probability estimator. J. Mater. Sci. Lett. 2003 2224 22(24), 1771–1773 (Dec. 2003). https://doi.org/10.1023/B:JMSL.0000005417.39465.35

  16. Chaurasiya, P.K., Ahmed, S., Warudkar, V.: Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument. Alexandria Eng. J. 57(4), 2299–2311 (2018). https://doi.org/10.1016/J.AEJ.2017.08.008. Dec.

    Article  Google Scholar 

  17. Mahmood, F.H., Resen, A.K., Khamees, A.B.: Wind characteristic analysis based on Weibull distribution of Al-Salman site, Iraq. Energy Rep. 6, 79–87 (2020). https://doi.org/10.1016/J.EGYR.2019.10.021. Feb.

    Article  Google Scholar 

  18. Strzelecki, P.: Determination of fatigue life for low probability of failure for different stress levels using 3-parameter Weibull distribution. Int. J. Fatigue 145, 106080 (2021). https://doi.org/10.1016/J.IJFATIGUE.2020.106080. Apr.

    Article  Google Scholar 

  19. Zhang, C.W.: Weibull parameter estimation and reliability analysis with zero-failure data from high-quality products. Reliab. Eng. Syst. Saf. 207, 107321 (2021). https://doi.org/10.1016/J.RESS.2020.107321. Mar.

    Article  Google Scholar 

  20. Annie Elisabeth Jebaseeli, E.: Monitoring the thermal behavior of induction motor using regression technique. In: 2017 Innovations in Power and Advanced Computing Technologies, i-PACT 2017, vol. 2017-January, pp. 1–4 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad Politécnica Salesiana, UPS, 170146, Quito, Ecuador

    Andrés Carvajal Andrade, Kevin Pazmiño Tintín, Johanna Celi & William Montalvo

Authors
  1. Andrés Carvajal Andrade
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kevin Pazmiño Tintín
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Johanna Celi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William Montalvo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Campus El Vecino, Universidad Politécnica Salesiana, Cuenca, Ecuador

    Vladimir Robles-Bykbaev

  2. Research Centre on Production Management and Engineering - CIGIP, Universitat Politècnica de València, Alcoy, Spain

    Josefa Mula

  3. Rua Imaculada Conceiçao, Pontifícia Universidade Católica do Paraná – PUCPR, Curitiba, Brazil

    Gilberto Reynoso-Meza

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Carvajal Andrade, A., Pazmiño Tintín, K., Celi, J., Montalvo, W. (2023). Low-Cost Fault Prediction System for a Rolling System on an Augmented Reality Platform with Cloud Communication. In: Robles-Bykbaev, V., Mula, J., Reynoso-Meza, G. (eds) Intelligent Technologies: Design and Applications for Society. CITIS 2022. Lecture Notes in Networks and Systems, vol 607. Springer, Cham. https://doi.org/10.1007/978-3-031-24327-1_5

Download citation

Publish with us

Policies and ethics

Search

Navigation