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’s 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.
%0 Journal Article
%1 kamm2022concept
%A Kamm, Simon
%A Sahlab, Nada
%A Jazdi, Nasser
%A Weyrich, Michael
%D 2022
%J 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME ‘22, Italy
%K 2022ias ias
%R 10.13140/RG.2.2.32657.10084
%T A Concept for Dynamic and Robust Machine Learning with Contex Modeling for Heterogeneous Manufacturing Data
%U https://www.ias.uni-stuttgart.de/dokumente/publikationen/2022-A_Concept_for_Dynamic_and_Robust_Machine_Learning_with_Contex_Modeling_for_Heterogeneous_Manufacturing_Data.pdf
%X 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’s 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.
@article{kamm2022concept,
abstract = {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’s 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.},
added-at = {2022-10-19T11:18:45.000+0200},
author = {Kamm, Simon and Sahlab, Nada and Jazdi, Nasser and Weyrich, Michael},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/22bfbef8391970cdf23233aedf4a5d8f5/taylansngerli},
doi = {10.13140/RG.2.2.32657.10084},
interhash = {e4424843ea83fb0d8a2597888307e497},
intrahash = {2bfbef8391970cdf23233aedf4a5d8f5},
journal = {16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME ‘22, Italy},
keywords = {2022ias ias},
timestamp = {2022-10-31T12:42:20.000+0100},
title = {A Concept for Dynamic and Robust Machine Learning with Contex Modeling for Heterogeneous Manufacturing Data },
url = {https://www.ias.uni-stuttgart.de/dokumente/publikationen/2022-A_Concept_for_Dynamic_and_Robust_Machine_Learning_with_Contex_Modeling_for_Heterogeneous_Manufacturing_Data.pdf},
year = 2022
}
