{"language":[{"iso":"eng"}],"publication":"Vibroengineering PROCEDIA","date_updated":"2022-12-05T12:51:08Z","type":"journal_article","year":"2022","intvolume":" 46","status":"public","author":[{"last_name":"Merkelbach","full_name":"Merkelbach, Silke","first_name":"Silke"},{"full_name":"Afroze, Lameya","last_name":"Afroze","first_name":"Lameya"},{"first_name":"Nils","last_name":"Janssen","full_name":"Janssen, Nils"},{"full_name":"von Enzberg, Sebastian","last_name":"von Enzberg","first_name":"Sebastian"},{"last_name":"Kühn","full_name":"Kühn, Arno","first_name":"Arno"},{"last_name":"Dumitrescu","full_name":"Dumitrescu, Roman","first_name":"Roman"}],"publisher":"JVE International Ltd.","volume":46,"publication_identifier":{"issn":["2345-0533","2538-8479"]},"department":[{"_id":"563"}],"_id":"34196","title":"Using vibration data to classify conditions in disk stack separators","citation":{"apa":"Merkelbach, S., Afroze, L., Janssen, N., von Enzberg, S., Kühn, A., & Dumitrescu, R. (2022). Using vibration data to classify conditions in disk stack separators. Vibroengineering PROCEDIA, 46, 21–26. https://doi.org/10.21595/vp.2022.23000","mla":"Merkelbach, Silke, et al. “Using Vibration Data to Classify Conditions in Disk Stack Separators.” Vibroengineering PROCEDIA, vol. 46, JVE International Ltd., 2022, pp. 21–26, doi:10.21595/vp.2022.23000.","ama":"Merkelbach S, Afroze L, Janssen N, von Enzberg S, Kühn A, Dumitrescu R. Using vibration data to classify conditions in disk stack separators. Vibroengineering PROCEDIA. 2022;46:21-26. doi:10.21595/vp.2022.23000","ieee":"S. Merkelbach, L. Afroze, N. Janssen, S. von Enzberg, A. Kühn, and R. Dumitrescu, “Using vibration data to classify conditions in disk stack separators,” Vibroengineering PROCEDIA, vol. 46, pp. 21–26, 2022, doi: 10.21595/vp.2022.23000.","chicago":"Merkelbach, Silke, Lameya Afroze, Nils Janssen, Sebastian von Enzberg, Arno Kühn, and Roman Dumitrescu. “Using Vibration Data to Classify Conditions in Disk Stack Separators.” Vibroengineering PROCEDIA 46 (2022): 21–26. https://doi.org/10.21595/vp.2022.23000.","short":"S. Merkelbach, L. Afroze, N. Janssen, S. von Enzberg, A. Kühn, R. Dumitrescu, Vibroengineering PROCEDIA 46 (2022) 21–26.","bibtex":"@article{Merkelbach_Afroze_Janssen_von Enzberg_Kühn_Dumitrescu_2022, title={Using vibration data to classify conditions in disk stack separators}, volume={46}, DOI={10.21595/vp.2022.23000}, journal={Vibroengineering PROCEDIA}, publisher={JVE International Ltd.}, author={Merkelbach, Silke and Afroze, Lameya and Janssen, Nils and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}, year={2022}, pages={21–26} }"},"user_id":"15782","publication_status":"published","doi":"10.21595/vp.2022.23000","page":"21-26","abstract":[{"text":"Mounting sensors in disk stack separators is often a major challenge due to the operating conditions. However, a process cannot be optimally monitored without sensors. Virtual sensors can be a solution to calculate the sought parameters from measurable values. We measured the vibrations of disk stack separators and applied machine learning (ML) to detect whether the separator contains only water or whether particles are also present. We combined seven ML classification algorithms with three feature engineering strategies and evaluated our model successfully on vibration data of an experimental disk stack separator. Our experimental results demonstrate that random forest in combination with manual feature engineering using domain specific knowledge about suitable features outperforms all other models with an accuracy of 91.27 %.","lang":"eng"}],"keyword":["General Medicine"],"date_created":"2022-12-05T12:47:22Z"}