@inproceedings{WiedholzHeiderNordsiecketal., author = {Wiedholz, Andreas and Heider, Michael and Nordsieck, Richard and Angerer, Andreas and Dietrich, Simon and H{\"a}hner, J{\"o}rg}, title = {CAD-based Grasp and Motion Planning for Process Automation in Fused Deposition Modelling}, series = {Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO}, booktitle = {Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO}, editor = {Gusikhin, Oleg and Nijmeijer, Henk and Madani, Kurosh}, publisher = {SCITEPRESS}, address = {Set{\´u}bal}, isbn = {978-989-758-522-7}, issn = {2184-2809}, doi = {10.5220/0010571204500458}, pages = {450 -- 458}, language = {en} } @inproceedings{MeitzHeiderSchoeleretal., author = {Meitz, Lukas and Heider, Michael and Sch{\"o}ler, Thorsten and H{\"a}hner, J{\"o}rg}, title = {On Data-Preprocessing for Effective Predictive Maintenance on Multi-Purpose Machines}, series = {Proceedings of the 12th International Conference on Data Science, Technology and Applications (DATA 2023)}, booktitle = {Proceedings of the 12th International Conference on Data Science, Technology and Applications (DATA 2023)}, editor = {Gusikhin, Oleg and Hammoudi, Slimane and Cuzzocrea, Alfredo}, publisher = {SciTePress}, isbn = {978-989-758-664-4}, issn = {2184-285X}, doi = {10.5220/0012146700003541}, pages = {606 -- 612}, abstract = {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.}, language = {en} }