On data-preprocessing for effective predictive maintenance on multi-purpose machines

  • Maintenance of complex machinery is time and resource intensive. Therefore, decreasing maintenance cycles by employing Predictive Maintenance (PdM) is sought after by many manufacturers of machines and can be a valuable selling point. However, currently PdM is a hard to solve problem getting increasingly harder with the complexity of the maintained system. One challenge is to adequately prepare data for model training and analysis. In this paper, we propose the use of expert knowledge–based preprocessing techniques to extend the standard data science–workflow. We define complex multi-purpose machinery as an application domain and test our proposed techniques on real-world data generated by numerous machines deployed in the wild. We find that our techniques enable and enhance model training.

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Author:Lukas MeitzORCiD, Michael HeiderORCiDGND, Thorsten SchölerORCiD, Jörg HähnerORCiDGND
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/110320
Parent Title (English):Proceedings of the 12th International Conference on Data Science, Technology and Applications, July 11-13, 2023, in Rome, Italy
Place of publication:Setúbal
Editor:Oleg Gusikhin, Slimane Hammoudi, Alfredo Cuzzocrea
Type:Conference Proceeding
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2023/12/20
First Page:606
Last Page:612
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Organic Computing
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung (mit Print on Demand)