{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T08:31:14Z","timestamp":1709368274322},"reference-count":16,"publisher":"Walter de Gruyter GmbH","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,3,26]]},"abstract":"<jats:title>Zusammenfassung<\/jats:title>\n               <jats:p>Die Kombination von unterschiedlichen Modellierungsans\u00e4tzen wird schon seit l\u00e4ngerer Zeit betrachtet \u2013 vor allem um sogenannte Grey-Box-Modelle zu erstellen. Im Rahmen der Digitalisierung von Produktionen entstehen jedoch bedingt durch eine gr\u00f6\u00dfere Datenbasis neue M\u00f6glichkeiten im Bereich der Modellbildung. Dieser Beitrag befasst sich daher mit einem Konzept, Modelle unterschiedlicher Produktlebenszyklusphasen sowie unterschiedlichen Typs miteinander zu kombinieren. Diese Modellkombination soll einen Mehrwert f\u00fcr den Betreiber darstellen, indem dieser detaillierte Simulationsmodelle w\u00e4hrend des Betriebs als Entscheidungsunterst\u00fctzung verwenden kann. Zun\u00e4chst werden die unterschiedlichen Modellierungsans\u00e4tze sowie deren Kombination eingef\u00fchrt. Daraufhin wird ein Konzept f\u00fcr das Training und die Betriebsphase der Modelle beschrieben. Abschlie\u00dfend wird das Potential des Ansatzes am Beispiel der thermischen Zustands\u00fcberwachung einer Asynchronmaschine dargestellt.<\/jats:p>","DOI":"10.1515\/auto-2018-0094","type":"journal-article","created":{"date-parts":[[2019,4,11]],"date-time":"2019-04-11T15:29:19Z","timestamp":1554996559000},"page":"183-192","source":"Crossref","is-referenced-by-count":1,"title":["Kombination unterschiedlicher Modellierungsans\u00e4tze f\u00fcr die betriebsbegleitende Simulation industrieller Prozesse"],"prefix":"10.1515","volume":"67","author":[{"given":"Christoph","family":"Bergs","sequence":"first","affiliation":[{"name":"1671 Siemens AG , Corporate Technology , Otto-Hahn-Ring 6 , M\u00fcnchen , Deutschland"}]},{"given":"Michael","family":"Heizmann","sequence":"additional","affiliation":[{"name":"Institut f\u00fcr industrielle Informationstechnik IIIT , Karlsruher Institut f\u00fcr Technologie , Karlsruhe , Germany"}]}],"member":"374","published-online":{"date-parts":[[2019,3,1]]},"reference":[{"key":"2023033110003431380_j_auto-2018-0094_ref_001_w2aab3b7b3b1b6b1ab1b5b1Aa","doi-asserted-by":"crossref","unstructured":"Naji Al-Messabi, Cindy Goh and Yun Li, Grey-Box Modeling for Photo-Voltaic Power Systems Using Dynamic Neural-Networks, in: 2017 Ninth Annual IEEE Green Technologies Conference (GreenTech), pp.\u2009267\u2013270, IEEE, 2017.","DOI":"10.1109\/GreenTech.2017.45"},{"key":"2023033110003431380_j_auto-2018-0094_ref_002_w2aab3b7b3b1b6b1ab1b5b2Aa","unstructured":"Alireza Hosseini, Milad Oshaghi and Sebastian Engell, IEEE International Conference on Control Applications (CCA), 2013; part of 2013 IEEE Multi-Conference on Systems and Control (MSC 2013), 28\u201330 Aug. 2013, Hyderabad, India IEEE, Piscataway, NJ, 2013."},{"key":"2023033110003431380_j_auto-2018-0094_ref_003_w2aab3b7b3b1b6b1ab1b5b3Aa","doi-asserted-by":"crossref","unstructured":"Jie Chen, Yibing Li and Fang Ye, Uncertain information fusion for gearbox fault diagnosis based on BP neural network and DS evidence theory, in: 2016 12th World Congress on Intelligent Control and Automation (WCICA), pp.\u20091372\u20131376, IEEE, 2016.","DOI":"10.1109\/WCICA.2016.7578248"},{"key":"2023033110003431380_j_auto-2018-0094_ref_004_w2aab3b7b3b1b6b1ab1b5b4Aa","unstructured":"M. Giselle Fern\u00e1ndez-Godino, Chanyoung Park, Nam-Ho Kim and Raphael T. Haftka, Review of multi-fidelity models."},{"key":"2023033110003431380_j_auto-2018-0094_ref_005_w2aab3b7b3b1b6b1ab1b5b5Aa","doi-asserted-by":"crossref","unstructured":"Michael Heizmann and Fernando Puente Le\u00f3n, Modellbildung in der Mess- und Automatisierungstechnik, tm \u2013 Technisches Messen 83(2) (2015), 63\u201365.","DOI":"10.1515\/teme-2015-0126"},{"key":"2023033110003431380_j_auto-2018-0094_ref_006_w2aab3b7b3b1b6b1ab1b5b6Aa","doi-asserted-by":"crossref","unstructured":"Petr Kadlec, Bogdan Gabrys and Sibylle Strandt, Data-driven Soft Sensors in the process industry, Computers & Chemical Engineering 33(4) (2009), 795\u2013814.10.1016\/j.compchemeng.2008.12.012","DOI":"10.1016\/j.compchemeng.2008.12.012"},{"key":"2023033110003431380_j_auto-2018-0094_ref_007_w2aab3b7b3b1b6b1ab1b5b7Aa","doi-asserted-by":"crossref","unstructured":"Rich\u00e1rd Kicsiny, Grey-box model for pipe temperature based on linear regression, International Journal of Heat and Mass Transfer 107 (2017), 13\u201320.10.1016\/j.ijheatmasstransfer.2016.11.033","DOI":"10.1016\/j.ijheatmasstransfer.2016.11.033"},{"key":"2023033110003431380_j_auto-2018-0094_ref_008_w2aab3b7b3b1b6b1ab1b5b8Aa","doi-asserted-by":"crossref","unstructured":"Krzysztof Kolanowski, Aleksandra \u015awietlicka, Rafa\u0142 Kapela, Janusz Pochmara and Andrzej Rybarczyk, Multisensor data fusion using Elman neural networks, Applied Mathematics and Computation 319 (2018), 236\u2013244.10.1016\/j.amc.2017.02.031","DOI":"10.1016\/j.amc.2017.02.031"},{"key":"2023033110003431380_j_auto-2018-0094_ref_009_w2aab3b7b3b1b6b1ab1b5b9Aa","doi-asserted-by":"crossref","unstructured":"Andreas Kroll, Computational Intelligence: Eine Einf\u00fchrung in Probleme, Methoden und technische Anwendungen, Oldenbourg, M\u00fcnchen, 2013.","DOI":"10.1524\/9783486737424"},{"key":"2023033110003431380_j_auto-2018-0094_ref_010_w2aab3b7b3b1b6b1ab1b5c10Aa","doi-asserted-by":"crossref","unstructured":"Jan Lunze, K\u00fcnstliche Intelligenz f\u00fcr Ingenieure: Methoden zur L\u00f6sung ingenieurtechnischer Probleme mit Hilfe von Regeln, logischen Formeln und Bayesnetzen, 3., \u00fcberarbeitete auflage ed, De Gruyter Studium, 2016.","DOI":"10.1515\/9783110448979"},{"key":"2023033110003431380_j_auto-2018-0094_ref_011_w2aab3b7b3b1b6b1ab1b5c11Aa","doi-asserted-by":"crossref","unstructured":"Mih\u00e1ly N\u00e9meth-Cs\u00f3ka, Thermisches Management elektrischer Maschinen: Messung, Modell und Energieoptimierung, Springer Vieweg, Wiesbaden, 2018.","DOI":"10.1007\/978-3-658-20133-3"},{"key":"2023033110003431380_j_auto-2018-0094_ref_012_w2aab3b7b3b1b6b1ab1b5c12Aa","unstructured":"Christian Pohlandt und Marcus Geimer, Thermische Modelle elektrischer Antriebsmaschinen unter dynamischen Lastanforderungen, LANDTECHNIK 70(4) (2015) 97\u2013112."},{"key":"2023033110003431380_j_auto-2018-0094_ref_013_w2aab3b7b3b1b6b1ab1b5c13Aa","unstructured":"Heinrich Ruser und Fernando Puente Le\u00f3n, Informationsfusion in der Messtechnik \u2013 \u00dcberblick und Taxonomie, Informationsfusion in der Mess- und Sensortechnik (J\u00fcrgen Beyerer, Fernando Puente Le\u00f3n and Klaus-Dieter Sommer, eds.), Universit\u00e4tsverlag Karlsruhe, Karlsruhe, 2007, pp.\u20091\u201320."},{"key":"2023033110003431380_j_auto-2018-0094_ref_014_w2aab3b7b3b1b6b1ab1b5c14Aa","doi-asserted-by":"crossref","unstructured":"Moritz von Stosch, Rui Oliveira, Joana Peres and Sebasti\u00e3o Feyo de Azevedo, Hybrid semi-parametric modeling in process systems engineering: Past, present and future, Computers & Chemical Engineering 60 (2014), 86\u2013101.10.1016\/j.compchemeng.2013.08.008","DOI":"10.1016\/j.compchemeng.2013.08.008"},{"key":"2023033110003431380_j_auto-2018-0094_ref_015_w2aab3b7b3b1b6b1ab1b5c15Aa","doi-asserted-by":"crossref","unstructured":"Bj\u00f6rn Wolff, Jan K\u00fchnert, Elke Lorenz, Oliver Kramer and Detlev Heinemann, Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data, Solar Energy 135 (2016), 197\u2013208.10.1016\/j.solener.2016.05.051","DOI":"10.1016\/j.solener.2016.05.051"},{"key":"2023033110003431380_j_auto-2018-0094_ref_016_w2aab3b7b3b1b6b1ab1b5c16Aa","doi-asserted-by":"crossref","unstructured":"Qi Zhou, Yan Wang, Seung-Kyum Choi, Ping Jiang, Xinyu Shao and Jiexiang Hu, A sequential multi-fidelity metamodeling approach for data regression, Knowledge-Based Systems 134 (2017), 199\u2013212.10.1016\/j.knosys.2017.07.033","DOI":"10.1016\/j.knosys.2017.07.033"}],"container-title":["at - Automatisierungstechnik"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/auto.2019.67.issue-3\/auto-2018-0094\/auto-2018-0094.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2018-0094\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2018-0094\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T10:38:08Z","timestamp":1680259088000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2018-0094\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,1]]},"references-count":16,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3,1]]},"published-print":{"date-parts":[[2019,3,26]]}},"alternative-id":["10.1515\/auto-2018-0094"],"URL":"https:\/\/doi.org\/10.1515\/auto-2018-0094","relation":{},"ISSN":["2196-677X","0178-2312"],"issn-type":[{"value":"2196-677X","type":"electronic"},{"value":"0178-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2019,3,1]]}}}