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Fault Detection Mechanism of a Predictive Maintenance System Based on Autoregressive Integrated Moving Average Models

  • Marta FernandesEmail author
  • Alda Canito
  • Juan Manuel Corchado
  • Goreti Marreiros
Conference paper
  • 485 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1003)

Abstract

The industrial world is amid a revolution, titled Industry 4.0, which entails the use of IoT technologies to enable the exchange of information between sensors, industrial machines and end users. A major issue in many industrial sectors is production inefficiency, with process downtime representing a loss for companies. Predictive maintenance, whereby maintenance is performed only when needed and before a failure occurs, has the potential to substantially reduce costs. This paper describes the fault detection mechanism of a predictive maintenance system developed for the metallurgic industry. Considering no previous information about faults is available, learning happens in an unsupervised manner. Imminent faults are predicted by estimating autoregressive integrated moving average models using real-world sensor data obtained from monitoring different machine components and parameters. The models’ outputs are fused to assess the significance of an anomaly (or anomalies) along the time domain and determine how likely a fault is to occur, with alarms being issued when the prospect of a fault is high enough.

Keywords

Predictive maintenance Anomaly detection ARIMA models Sensor data 

Notes

Acknowledgements

The authors wish to acknowledge the Portuguese funding institution FCT - Fundação para a Ciência e a Tecnologia for supporting their research through project UID/EEA/00760/2019 and Ph.D. Scholarship SFRH/BD/136253/2018.

References

  1. 1.
    Evans, P.C., Annunziata, M.: Industrial internet: pushing the boundaries of minds and machines (2012)Google Scholar
  2. 2.
    Boyes, H., Hallaq, B., Cunningham, J., Watson, T.: The industrial internet of things (IIoT): an analysis framework. Comput. Ind. 101, 1–12 (2018)CrossRefGoogle Scholar
  3. 3.
    Holmberg, K., Adgar, A., Arnaiz, A., Jantunen, E., Mascolo, J., Mekid, S. (eds.): E-maintenance. Springer, London (2010)Google Scholar
  4. 4.
    Williamson, J.: Unplanned downtime affecting 82% of businesses. https://www.themanufacturer.com/articles/unplanned-downtime-affecting-82-businesses/. Accessed 25 Jan 2019
  5. 5.
    Aboelmaged, M.: Predicting e-readiness at firm-level: an analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms. Int. J. Inf. Manag. 34, 639–651 (2014)CrossRefGoogle Scholar
  6. 6.
    Selcuk, S.: Predictive maintenance, its implementation and latest trends. In: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 231, pp. 1670–1679 (2017)CrossRefGoogle Scholar
  7. 7.
    Muller, A., Marquez, A., Iung, B.: On the concept of e-maintenance: Review and current research. Reliab. Eng. Syst. Saf. 93, 1165–1187 (2008)CrossRefGoogle Scholar
  8. 8.
    Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17, 2347–2376 (2015)CrossRefGoogle Scholar
  9. 9.
    Lee, J., Jin, C., Bagheri, B.: Cyber physical systems for predictive production systems. Prod. Eng. 11, 155–165 (2017)CrossRefGoogle Scholar
  10. 10.
    Krempl, G., Žliobaite, I., Brzeziński, D., Hüllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M., Stefanowski, J.: Open challenges for data stream mining research. ACM SIGKDD Explor. Newsl. 16, 1–10 (2014)CrossRefGoogle Scholar
  11. 11.
    Gama, J.: A survey on learning from data streams: current and future trends. Prog. Artif. Intell. 1, 45–55 (2012)CrossRefGoogle Scholar
  12. 12.
    Gupta, M., Gao, J., Aggarwal, C., Han, J.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26, 2250–2267 (2014)CrossRefGoogle Scholar
  13. 13.
    Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45, 12 (2012)CrossRefGoogle Scholar
  14. 14.
    Ahmed, N.K., Atiya, A.F., Gayar, N.E., El-Shishiny, H.: An empirical comparison of machine learning models for time series forecasting. Econom. Rev. 29, 594–621 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Jardine, A.K.S., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20, 1483–1510 (2006)CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Box, G.E.P., Jenkins, G.M.: Time series analysis: forecasting and control. Holden-Day (1970)Google Scholar
  18. 18.
    Canito, A., Fernandes, M., Conceição, L., Praça, I., Santos, M., Rato, R., Cardeal, G., Leiras, F., Marreiros, G.: An architecture for proactive maintenance in the machinery industry. In: Advances in Intelligent Systems and Computing, pp. 254–262. Springer (2017)Google Scholar
  19. 19.
    Fernandes, M., Canito, A., Bolón-Canedo, V., Conceição, L., Praça, I., Marreiros, G.: Data analysis and feature selection for predictive maintenance: a case-study in the metallurgic industry. Int. J. Inf. Manage. 46, 252–262 (2018)CrossRefGoogle Scholar
  20. 20.
    Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications. Springer, Cham (2017)CrossRefGoogle Scholar
  21. 21.
    Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice, 2nd edn. OTexts, Melbourne (2018)Google Scholar
  22. 22.
    Burnham, K.P., Anderson, D.R.: Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and DevelopmentPolytechnic of Porto (ISEP/IPP)PortoPortugal
  2. 2.BISITE Research CentreUniversity of Salamanca (USAL)SalamancaSpain

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