[{"date_updated":"2022-08-24T12:45:39Z","author":[{"id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede","first_name":"Alexander"},{"last_name":"Gehring","full_name":"Gehring, Lukas","first_name":"Lukas"},{"last_name":"Tornede","id":"40795","full_name":"Tornede, Tanja","first_name":"Tanja"},{"id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"}],"date_created":"2022-04-12T11:55:18Z","title":"Algorithm Selection on a Meta Level","year":"2022","citation":{"chicago":"Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” <i>Machine Learning</i>, 2022.","ieee":"A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” <i>Machine Learning</i>. 2022.","ama":"Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection on a Meta Level. <i>Machine Learning</i>. Published online 2022.","short":"A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning (2022).","mla":"Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” <i>Machine Learning</i>, 2022.","bibtex":"@article{Tornede_Gehring_Tornede_Wever_Hüllermeier_2022, title={Algorithm Selection on a Meta Level}, journal={Machine Learning}, author={Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2022} }","apa":"Tornede, A., Gehring, L., Tornede, T., Wever, M. D., &#38; Hüllermeier, E. (2022). Algorithm Selection on a Meta Level. In <i>Machine Learning</i>."},"external_id":{"arxiv":["2107.09414"]},"_id":"30865","project":[{"name":"SFB 901: SFB 901","_id":"1"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"user_id":"38209","language":[{"iso":"eng"}],"publication":"Machine Learning","type":"preprint","abstract":[{"text":"The problem of selecting an algorithm that appears most suitable for a\r\nspecific instance of an algorithmic problem class, such as the Boolean\r\nsatisfiability problem, is called instance-specific algorithm selection. Over\r\nthe past decade, the problem has received considerable attention, resulting in\r\na number of different methods for algorithm selection. Although most of these\r\nmethods are based on machine learning, surprisingly little work has been done\r\non meta learning, that is, on taking advantage of the complementarity of\r\nexisting algorithm selection methods in order to combine them into a single\r\nsuperior algorithm selector. In this paper, we introduce the problem of meta\r\nalgorithm selection, which essentially asks for the best way to combine a given\r\nset of algorithm selectors. We present a general methodological framework for\r\nmeta algorithm selection as well as several concrete learning methods as\r\ninstantiations of this framework, essentially combining ideas of meta learning\r\nand ensemble learning. In an extensive experimental evaluation, we demonstrate\r\nthat ensembles of algorithm selectors can significantly outperform single\r\nalgorithm selectors and have the potential to form the new state of the art in\r\nalgorithm selection.","lang":"eng"}],"status":"public"},{"status":"public","type":"report","language":[{"iso":"ger"}],"ddc":["004"],"user_id":"40795","department":[{"_id":"34"},{"_id":"7"},{"_id":"534"}],"_id":"36227","citation":{"short":"B. Hammer, E. Hüllermeier, V. Lohweg, A. Schneider, W. Schenck, U. Kuhl, M. Braun, A. Pfeifer, C.-A. Holst, M. Schmidt, G. Schomaker, T. Tornede, Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens, 2022.","mla":"Hammer, Barbara, et al. <i>Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens</i>. 2022, doi:<a href=\"https://doi.org/10.4119/unibi/2965622\">10.4119/unibi/2965622</a>.","bibtex":"@book{Hammer_Hüllermeier_Lohweg_Schneider_Schenck_Kuhl_Braun_Pfeifer_Holst_Schmidt_et al._2022, title={Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens}, DOI={<a href=\"https://doi.org/10.4119/unibi/2965622\">10.4119/unibi/2965622</a>}, author={Hammer, Barbara and Hüllermeier, Eyke and Lohweg, Volker and Schneider, Alexander and Schenck, Wolfram and Kuhl, Ulrike and Braun, Marco and Pfeifer, Anton and Holst, Christoph-Alexander and Schmidt, Malte and et al.}, year={2022} }","ama":"Hammer B, Hüllermeier E, Lohweg V, et al. <i>Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens</i>.; 2022. doi:<a href=\"https://doi.org/10.4119/unibi/2965622\">10.4119/unibi/2965622</a>","apa":"Hammer, B., Hüllermeier, E., Lohweg, V., Schneider, A., Schenck, W., Kuhl, U., Braun, M., Pfeifer, A., Holst, C.-A., Schmidt, M., Schomaker, G., &#38; Tornede, T. (2022). <i>Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens</i>. <a href=\"https://doi.org/10.4119/unibi/2965622\">https://doi.org/10.4119/unibi/2965622</a>","ieee":"B. Hammer <i>et al.</i>, <i>Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens</i>. 2022.","chicago":"Hammer, Barbara, Eyke Hüllermeier, Volker Lohweg, Alexander Schneider, Wolfram Schenck, Ulrike Kuhl, Marco Braun, et al. <i>Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens</i>, 2022. <a href=\"https://doi.org/10.4119/unibi/2965622\">https://doi.org/10.4119/unibi/2965622</a>."},"year":"2022","has_accepted_license":"1","doi":"10.4119/unibi/2965622","title":"Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens","author":[{"last_name":"Hammer","full_name":"Hammer, Barbara","first_name":"Barbara"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"},{"last_name":"Lohweg","full_name":"Lohweg, Volker","first_name":"Volker"},{"last_name":"Schneider","full_name":"Schneider, Alexander","first_name":"Alexander"},{"full_name":"Schenck, Wolfram","last_name":"Schenck","first_name":"Wolfram"},{"full_name":"Kuhl, Ulrike","last_name":"Kuhl","first_name":"Ulrike"},{"first_name":"Marco","last_name":"Braun","full_name":"Braun, Marco"},{"last_name":"Pfeifer","full_name":"Pfeifer, Anton","first_name":"Anton"},{"full_name":"Holst, Christoph-Alexander","last_name":"Holst","first_name":"Christoph-Alexander"},{"first_name":"Malte","last_name":"Schmidt","full_name":"Schmidt, Malte"},{"first_name":"Gunnar","full_name":"Schomaker, Gunnar","last_name":"Schomaker"},{"first_name":"Tanja","id":"40795","full_name":"Tornede, Tanja","last_name":"Tornede"}],"date_created":"2023-01-11T15:00:00Z","date_updated":"2023-01-11T15:20:40Z"},{"citation":{"bibtex":"@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2021} }","short":"T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021.","mla":"Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021.","apa":"Tornede, T., Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. Genetic and Evolutionary Computation Conference.","chicago":"Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021.","ieee":"T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented at the Genetic and Evolutionary Computation Conference, 2021.","ama":"Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. ; 2021."},"year":"2021","conference":{"start_date":"2021-07-10","name":"Genetic and Evolutionary Computation Conference","end_date":"2021-07-14"},"title":"Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance","date_created":"2021-03-26T09:14:19Z","author":[{"first_name":"Tanja","last_name":"Tornede","full_name":"Tornede, Tanja","id":"40795"},{"full_name":"Tornede, Alexander","id":"38209","last_name":"Tornede","first_name":"Alexander"},{"first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","id":"33176"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"}],"date_updated":"2022-01-06T06:55:06Z","status":"public","type":"conference","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","language":[{"iso":"eng"}],"user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"_id":"21570"},{"type":"preprint","publication":"arXiv:2111.05850","status":"public","abstract":[{"lang":"eng","text":"Automated machine learning (AutoML) strives for the automatic configuration\r\nof machine learning algorithms and their composition into an overall (software)\r\nsolution - a machine learning pipeline - tailored to the learning task\r\n(dataset) at hand. Over the last decade, AutoML has developed into an\r\nindependent research field with hundreds of contributions. While AutoML offers\r\nmany prospects, it is also known to be quite resource-intensive, which is one\r\nof its major points of criticism. The primary cause for a high resource\r\nconsumption is that many approaches rely on the (costly) evaluation of many\r\nmachine learning pipelines while searching for good candidates. This problem is\r\namplified in the context of research on AutoML methods, due to large scale\r\nexperiments conducted with many datasets and approaches, each of them being run\r\nwith several repetitions to rule out random effects. In the spirit of recent\r\nwork on Green AI, this paper is written in an attempt to raise the awareness of\r\nAutoML researchers for the problem and to elaborate on possible remedies. To\r\nthis end, we identify four categories of actions the community may take towards\r\nmore sustainable research on AutoML, i.e. Green AutoML: design of AutoML\r\nsystems, benchmarking, transparency and research incentives."}],"user_id":"38209","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"project":[{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"_id":"30866","external_id":{"arxiv":["2111.05850"]},"language":[{"iso":"eng"}],"citation":{"apa":"Tornede, T., Tornede, A., Hanselle, J. M., Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions. In <i>arXiv:2111.05850</i>.","bibtex":"@article{Tornede_Tornede_Hanselle_Wever_Mohr_Hüllermeier_2021, title={Towards Green Automated Machine Learning: Status Quo and Future Directions}, journal={arXiv:2111.05850}, author={Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2021} }","short":"T. Tornede, A. Tornede, J.M. Hanselle, M.D. Wever, F. Mohr, E. Hüllermeier, ArXiv:2111.05850 (2021).","mla":"Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” <i>ArXiv:2111.05850</i>, 2021.","ama":"Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards Green Automated Machine Learning: Status Quo and Future Directions. <i>arXiv:211105850</i>. Published online 2021.","ieee":"T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier, “Towards Green Automated Machine Learning: Status Quo and Future Directions,” <i>arXiv:2111.05850</i>. 2021.","chicago":"Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” <i>ArXiv:2111.05850</i>, 2021."},"year":"2021","author":[{"last_name":"Tornede","id":"40795","full_name":"Tornede, Tanja","first_name":"Tanja"},{"last_name":"Tornede","full_name":"Tornede, Alexander","id":"38209","first_name":"Alexander"},{"last_name":"Hanselle","orcid":"0000-0002-1231-4985","id":"43980","full_name":"Hanselle, Jonas Manuel","first_name":"Jonas Manuel"},{"id":"33176","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_created":"2022-04-12T11:57:15Z","date_updated":"2022-04-12T12:01:23Z","title":"Towards Green Automated Machine Learning: Status Quo and Future Directions"},{"language":[{"iso":"eng"}],"project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"SFB 901","_id":"1"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"17424","user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"status":"public","type":"conference","publication":"Proceedings of the ECMLPKDD 2020","title":"AutoML for Predictive Maintenance: One Tool to RUL Them All","doi":"10.1007/978-3-030-66770-2_8","conference":{"name":"IOTStream Workshop @ ECMLPKDD 2020"},"date_updated":"2022-01-06T06:53:11Z","author":[{"first_name":"Tanja","id":"40795","full_name":"Tornede, Tanja","last_name":"Tornede"},{"first_name":"Alexander","last_name":"Tornede","full_name":"Tornede, Alexander","id":"38209"},{"first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","id":"33176"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"date_created":"2020-07-28T09:17:41Z","year":"2020","citation":{"ieee":"T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML for Predictive Maintenance: One Tool to RUL Them All,” presented at the IOTStream Workshop @ ECMLPKDD 2020, 2020, doi: <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>.","chicago":"Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” In <i>Proceedings of the ECMLPKDD 2020</i>, 2020. <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">https://doi.org/10.1007/978-3-030-66770-2_8</a>.","ama":"Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive Maintenance: One Tool to RUL Them All. In: <i>Proceedings of the ECMLPKDD 2020</i>. ; 2020. doi:<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>","apa":"Tornede, T., Tornede, A., Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. <i>Proceedings of the ECMLPKDD 2020</i>. IOTStream Workshop @ ECMLPKDD 2020. <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">https://doi.org/10.1007/978-3-030-66770-2_8</a>","short":"T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020.","mla":"Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” <i>Proceedings of the ECMLPKDD 2020</i>, 2020, doi:<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>.","bibtex":"@inproceedings{Tornede_Tornede_Wever_Mohr_Hüllermeier_2020, title={AutoML for Predictive Maintenance: One Tool to RUL Them All}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>}, booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }"}},{"status":"public","abstract":[{"lang":"eng","text":"<jats:p>The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.</jats:p>"}],"publication":"Sensors","type":"journal_article","language":[{"iso":"eng"}],"article_number":"2099","user_id":"40795","_id":"17426","citation":{"bibtex":"@article{Hoffmann_Wildermuth_Gitzel_Boyaci_Gebhardt_Kaul_Amihai_Forg_Suriyah_Leibfried_et al._2020, title={Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions}, DOI={<a href=\"https://doi.org/10.3390/s20072099\">10.3390/s20072099</a>}, number={2099}, journal={Sensors}, author={Hoffmann, Martin W. and Wildermuth, Stephan and Gitzel, Ralf and Boyaci, Aydin and Gebhardt, Jörg and Kaul, Holger and Amihai, Ido and Forg, Bodo and Suriyah, Michael and Leibfried, Thomas and et al.}, year={2020} }","short":"M.W. Hoffmann, S. Wildermuth, R. Gitzel, A. Boyaci, J. Gebhardt, H. Kaul, I. Amihai, B. Forg, M. Suriyah, T. Leibfried, V. Stich, J. Hicking, M. Bremer, L. Kaminski, D. Beverungen, P. zur Heiden, T. Tornede, Sensors (2020).","mla":"Hoffmann, Martin W., et al. “Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions.” <i>Sensors</i>, 2099, 2020, doi:<a href=\"https://doi.org/10.3390/s20072099\">10.3390/s20072099</a>.","apa":"Hoffmann, M. W., Wildermuth, S., Gitzel, R., Boyaci, A., Gebhardt, J., Kaul, H., … Tornede, T. (2020). Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions. <i>Sensors</i>. <a href=\"https://doi.org/10.3390/s20072099\">https://doi.org/10.3390/s20072099</a>","ama":"Hoffmann MW, Wildermuth S, Gitzel R, et al. Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions. <i>Sensors</i>. 2020. doi:<a href=\"https://doi.org/10.3390/s20072099\">10.3390/s20072099</a>","chicago":"Hoffmann, Martin W., Stephan Wildermuth, Ralf Gitzel, Aydin Boyaci, Jörg Gebhardt, Holger Kaul, Ido Amihai, et al. “Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions.” <i>Sensors</i>, 2020. <a href=\"https://doi.org/10.3390/s20072099\">https://doi.org/10.3390/s20072099</a>.","ieee":"M. W. Hoffmann <i>et al.</i>, “Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions,” <i>Sensors</i>, 2020."},"year":"2020","publication_identifier":{"issn":["1424-8220"]},"publication_status":"published","doi":"10.3390/s20072099","main_file_link":[{"url":"https://www.mdpi.com/1424-8220/20/7/2099","open_access":"1"}],"title":"Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions","author":[{"full_name":"Hoffmann, Martin W.","last_name":"Hoffmann","first_name":"Martin W."},{"full_name":"Wildermuth, Stephan","last_name":"Wildermuth","first_name":"Stephan"},{"first_name":"Ralf","last_name":"Gitzel","full_name":"Gitzel, Ralf"},{"first_name":"Aydin","full_name":"Boyaci, Aydin","last_name":"Boyaci"},{"last_name":"Gebhardt","full_name":"Gebhardt, Jörg","first_name":"Jörg"},{"last_name":"Kaul","full_name":"Kaul, Holger","first_name":"Holger"},{"first_name":"Ido","full_name":"Amihai, Ido","last_name":"Amihai"},{"first_name":"Bodo","last_name":"Forg","full_name":"Forg, Bodo"},{"last_name":"Suriyah","full_name":"Suriyah, Michael","first_name":"Michael"},{"full_name":"Leibfried, Thomas","last_name":"Leibfried","first_name":"Thomas"},{"first_name":"Volker","full_name":"Stich, Volker","last_name":"Stich"},{"first_name":"Jan","full_name":"Hicking, Jan","last_name":"Hicking"},{"first_name":"Martin","last_name":"Bremer","full_name":"Bremer, Martin"},{"first_name":"Lars","full_name":"Kaminski, Lars","last_name":"Kaminski"},{"full_name":"Beverungen, Daniel","id":"59677","last_name":"Beverungen","first_name":"Daniel"},{"last_name":"zur Heiden","id":"64394","full_name":"zur Heiden, Philipp","first_name":"Philipp"},{"first_name":"Tanja","full_name":"Tornede, Tanja","id":"40795","last_name":"Tornede"}],"date_created":"2020-07-28T09:47:36Z","oa":"1","date_updated":"2022-01-06T06:53:11Z"}]