{"id":3114,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3114"},"modified":"2022-07-26T13:05:07","modified_gmt":"2022-07-26T11:05:07","slug":"a-concept-for-dynamic-and-robust-machine-learning-with-contex-modeling-for-heterogeneous-manufacturing-data","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/cyber-physical-systems-digital-manufacturing-2\/a-concept-for-dynamic-and-robust-machine-learning-with-contex-modeling-for-heterogeneous-manufacturing-data\/","title":{"rendered":"A Concept for Dynamic and Robust Machine Learning with Contex Modeling for Heterogeneous Manufacturing Data"},"content":{"rendered":"\n

by Simon Kamm, Nada Sahlab, Nasser Jazdi, Michael Weyrich (Germany)<\/em><\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

With the increasing amount of available and connected data sources, industrial automation applications such as condition monitoring of a production machine can be improved by considering various data. To gain insights from this data and make it useable, heterogeneous data has to be analyzed intensively. Limited machine learning approaches exist in industrial automation and manufacturing for analyzing data acquired from multiple sources. In this paper, first, a suitable concept for handling heterogeneous data from integration to analysis is presented as well as a multi-layer architecture for the concept\u00d5s realization. The architecture encapsulates functionalities into the different layers and allows easy extendability and modifiability. Afterwards, a context modeling approach for managing heterogeneous data and existing approaches and algorithms for analyzing this data robustly and dynamically analyzing it are presented.<\/p>\n\n\n\n

Keywords<\/strong>: Machine Learning; Dynamic and Robust Machine Learning; Heterogeneous Data; Context Modeling<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n