TY - CHAP A1 - Meitz, Lukas A1 - Heider, Michael A1 - Schöler, Thorsten A1 - Hähner, Jörg T1 - A Taxonomy for Complexity Estimation of Machine Data in Machine Health Applications T2 - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics KW - product automation systems KW - predictive maintenance KW - condition monitoring KW - complexity KW - taxonomy Y1 - 2024 UR - https://www.scitepress.org/publishedPapers/2024/129949/pdf/index.html SN - 978-989-758-717-7 U6 - https://doi.org/10.5220/0012994900003822 SN - 2184-2809 N1 - Beitrag: 21st International Conference on Informatics in Control, Automation and Robotics (ICINCO 2024), Porto, Portugal, 18.11.2024 - 20.11.2024 VL - 1 SP - 341 EP - 350 PB - SCITEPRESS - Science and Technology Publications ER - TY - CHAP A1 - Meitz, Lukas A1 - Heider, Michael A1 - Schöler, Thorsten A1 - Hähner, Jörg ED - Gusikhin, Oleg ED - Hammoudi, Slimane ED - Cuzzocrea, Alfredo T1 - On Data-Preprocessing for Effective Predictive Maintenance on Multi-Purpose Machines T2 - Proceedings of the 12th International Conference on Data Science, Technology and Applications (DATA 2023) N2 - 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 pplication domain and test our proposed techniques on real-world data generated by numerous achines deployed in the wild. We find that our techniques enable and enhance model training. KW - predictive maintenance KW - data preprocessing KW - multi-purpose machines Y1 - 2023 SN - 978-989-758-664-4 U6 - https://doi.org/10.5220/0012146700003541 SN - 2184-285X SP - 606 EP - 612 PB - SciTePress ER -