Abstract
Through technological advents from Industry 4.0 and the Internet of Things, as well as new Big Data solutions, predictive maintenance begins to play a strategic role in the increasing operational performance of any industrial facility. Equipment failures can be very costly and have catastrophic consequences. In its basic concept, Predictive maintenance allows minimizing equipment faults or service disruptions, presenting promising cost savings. This paper presents a data-driven approach, based on multiple-instance learning, to predict malfunctions in End-of-Line Testing Systems through the extraction of operational logs, which, while not designed to predict failures, contains valid information regarding their operational mode over time. For the case study performed, a real-life dataset was used containing thousands of log messages, collected in a real automotive industry environment. The insights gained from mining this type of data will be shared in this paper, highlighting the main challenges and benefits, as well as good recommendations, and best practices for the appropriate usage of machine learning techniques and analytics tools that can be implemented in similar industrial environments.
Supported by Continental Advanced Antenna (CAA), Portugal (a Continental AG Group Company).
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
Notes
- 1.
To demonstrate this approach, the authors used datasets from in-service EOL devices, nevertheless, this methodology can also be applied to other research fields, as e.g., IT infrastructures or any industrial equipment, in which such sort of logs could be collected.
References
Yan, J., Meng, Y., Lu, L., Li, L.: Industrial big data in an Industry 4.0 environment: challenges, schemes, and applications for predictive maintenance. IEEE Access 5, 23484–23491 (2017). IEEE Special Section on Complex System Health Management Based on Condition Monitoring and Test Data, USA
Merkt, O.: On the use of predictive models for improving the quality of Industrial maintenance: an analytical literature review of maintenance strategies. In: 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), Germany, vol. 18, pp. 693–704. ACSIS (2019)
Krupitzer, C., et al.: A survey on predictive maintenance for Industry 4.0. Cornell University Computer Science Article, Germany (2020). https://arxiv.org/abs/2002.08224
Weiting, Z., Dong, Y., Hongchao, W.: Data-driven methods for predictive maintenance of industrial equipment: a survey. IEEE Syst. J. 13, 2213–2227 (2019)
Cachada, A.: Intelligent and predictive maintenance in manufacturing systems. IPB MSc dissertation, Portugal (2018). http://hdl.handle.net/10198/18301
Markiewicz, M., Wielgosz, M., Tabaczynski, W., Konieczny, T., Kowalczyk, L.: Predictive maintenance of induction motors using ultra-low power wireless sensors and compressed recurrent neural networks. IEEE Access 7, 178891–178902 (2019). IEEE Special Section on Intelligent and Cognitive Techniques for Internet of Things, Poland
Ding, H., Yang , L., Yang, Z.: A predictive maintenance method for shearer key parts based on qualitative and quantitative analysis of monitoring data. IEEE Access 7, 108684–108702 (2019). IEEE Special Section on Advances in Prognostics and System Health Management, USA
Li, Z., Goebel, K., Wu, D.: Degradation modeling and remaining useful life prediction of aircraft engines using ensemble learning. J. Eng. Gas Turbines Power Trans. ASME 141, 041008 (2019)
Sipos, R., Fradkin, D., Moerchen, F., Wang, Z.: Log-based predictive maintenance. In: KDD 2014: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, USA, pp. 1867–1876. ACM (2014)
European/Portuguese Standard. Norma Portuguesa NP EN 13306:2007 (Registo no. 20294). (Portugal) IPQ, Terminologia da Manutençã (2007). https://biblioteca.isel.pt/cgi-bin/koha/opac-ISBDdetail.pl?biblionumber=20294
Gutschi, C., Furian, N., Suschnigg, J., Neubacher, D., Voessner, S.: Log-based predictive maintenance in discrete parts manufacturing. Procedia CIRP 79, 528–533 (2019). 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Italy
Kadechkar, A., Moreno-Eguilaz, M., Riba, J.-R., Capelli, F.: Low-cost online contact resistance measurement of power connectors to ease predictive maintenance. IEEE Trans. Instrum. Meas. 5(12), 4825–4833 (2019)
Proto, S., et al.: REDTag: a predictive maintenance framework for parcel delivery services. IEEE Access 8, 14953–14964 (2020). IEEE Special Section on Intelligent and Cognitive Techniques for Internet of Things, Italy
Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE Access 4, 1942–1948 (1995). Proceedings of ICNN’95 - International Conference on Neural Networks, Australia
Li, Z., Wu, D., Hu, C., Terpenny, J.: An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction. Reliab. Eng. Syst. Saf. 184, 110–122 (2019)
Goebel, K., Eklund, N., Bonanni, P.: Fusing competing prediction algorithms for prognostics. In: Accepted for Presentation and Publication in Proceedings of 2005 IEEE Aerospace Conference, USA. AERO, Big Sky, MT, p. 10 (2006). https://doi.org/10.1109/AERO
Chen, H.: A multiple model prediction algorithm for CNC machine wear PHM. Int. J. Prognostics Health Manage (2011). http://works.bepress.com/huimin_chen/5/
DEUTRONIC. End of Line (EOL) e-motor test system. DEUTRONIC, Modern test concepts for various electro mobility components, Germany (2020). https://www.deutronic.com/testsystem/end-of-line-eol-motor-test-system/
Acknowledgements
This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project UIDB/50014/2020. Special thanks to all the personnel involved, that gave the support necessary to make the publishing of this work possible, in particular to the Continental Advanced Antenna and the University of Trás-os-Montes and Alto Douro.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Vicêncio, D., Silva, H., Soares, S., Filipe, V., Valente, A. (2021). An Intelligent Predictive Maintenance Approach Based on End-of-Line Test Logfiles in the Automotive Industry. In: Peñalver, L., Parra, L. (eds) Industrial IoT Technologies and Applications. Industrial IoT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 365. Springer, Cham. https://doi.org/10.1007/978-3-030-71061-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-71061-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71060-6
Online ISBN: 978-3-030-71061-3
eBook Packages: Computer ScienceComputer Science (R0)