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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Evans, P.C., Annunziata, M.: Industrial internet: pushing the boundaries of minds and machines (2012)
Boyes, H., Hallaq, B., Cunningham, J., Watson, T.: The industrial internet of things (IIoT): an analysis framework. Comput. Ind. 101, 1–12 (2018)
Holmberg, K., Adgar, A., Arnaiz, A., Jantunen, E., Mascolo, J., Mekid, S. (eds.): E-maintenance. Springer, London (2010)
Williamson, J.: Unplanned downtime affecting 82% of businesses. https://www.themanufacturer.com/articles/unplanned-downtime-affecting-82-businesses/. Accessed 25 Jan 2019
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)
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)
Muller, A., Marquez, A., Iung, B.: On the concept of e-maintenance: Review and current research. Reliab. Eng. Syst. Saf. 93, 1165–1187 (2008)
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)
Lee, J., Jin, C., Bagheri, B.: Cyber physical systems for predictive production systems. Prod. Eng. 11, 155–165 (2017)
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)
Gama, J.: A survey on learning from data streams: current and future trends. Prog. Artif. Intell. 1, 45–55 (2012)
Gupta, M., Gao, J., Aggarwal, C., Han, J.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26, 2250–2267 (2014)
Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45, 12 (2012)
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)
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)
InValuePT. http://www.invalue.com.pt/
Box, G.E.P., Jenkins, G.M.: Time series analysis: forecasting and control. Holden-Day (1970)
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)
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)
Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications. Springer, Cham (2017)
Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice, 2nd edn. OTexts, Melbourne (2018)
Burnham, K.P., Anderson, D.R.: Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fernandes, M., Canito, A., Corchado, J.M., Marreiros, G. (2020). Fault Detection Mechanism of a Predictive Maintenance System Based on Autoregressive Integrated Moving Average Models. In: Herrera, F., Matsui , K., RodrĂguez-González, S. (eds) Distributed Computing and Artificial Intelligence, 16th International Conference. DCAI 2019. Advances in Intelligent Systems and Computing, vol 1003 . Springer, Cham. https://doi.org/10.1007/978-3-030-23887-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-23887-2_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23886-5
Online ISBN: 978-3-030-23887-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)