Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vastly improve a transfer’s results. In this paper, a transfer case selection based upon clustering is presented. Founded on a survey of clustering algorithms, the BIRCH algorithm is selected for this purpose. It is evaluated on an industrial time series dataset from a discrete manufacturing scenario. Results underline the approaches’ applicability caused by its results’ reproducibility and practical indifference to sequence, size and dimensionality of (sub-)datasets to be clustered sequentially.
%0 Generic
%1 maschler2022towards
%A Maschler, Benjamin
%A Knodel, Tim
%A Weyrich, Michael
%B 27th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Stuttgart, Germany, September 2022
%D 2022
%K 2022ias ias
%T Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection
%X Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vastly improve a transfer’s results. In this paper, a transfer case selection based upon clustering is presented. Founded on a survey of clustering algorithms, the BIRCH algorithm is selected for this purpose. It is evaluated on an industrial time series dataset from a discrete manufacturing scenario. Results underline the approaches’ applicability caused by its results’ reproducibility and practical indifference to sequence, size and dimensionality of (sub-)datasets to be clustered sequentially.
@conference{maschler2022towards,
abstract = {Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vastly improve a transfer’s results. In this paper, a transfer case selection based upon clustering is presented. Founded on a survey of clustering algorithms, the BIRCH algorithm is selected for this purpose. It is evaluated on an industrial time series dataset from a discrete manufacturing scenario. Results underline the approaches’ applicability caused by its results’ reproducibility and practical indifference to sequence, size and dimensionality of (sub-)datasets to be clustered sequentially.},
added-at = {2022-10-19T11:11:12.000+0200},
author = {Maschler, Benjamin and Knodel, Tim and Weyrich, Michael},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/22f413febb3f2466711e55ce4f564a6b8/taylansngerli},
booktitle = {27th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Stuttgart, Germany, September 2022},
interhash = {aacc4b576345c2b9ba8f6fa31e91fef9},
intrahash = {2f413febb3f2466711e55ce4f564a6b8},
keywords = {2022ias ias},
timestamp = {2022-10-28T09:02:08.000+0200},
title = {Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection },
year = 2022
}
