by Werner Zellinger, Thomas Grubinger, Michael Zwick, Edwin Lughofer, Holger Schöner, Thomas Natschläger, Susanne Saminger-Platz
Abstract:
This paper describes a new transfer learning method for modeling sensor time series following multiple different distributions, e.g. originating from multiple different tool settings. The method aims at removing distribution specific information before the modeling of the individual time series takes place. This is done by mapping the data to a new space such that the representations of different distributions are aligned. Domain knowledge is incorporated by means of corresponding parameters, e.g. physical dimensions of tool settings. Results on a real-world problem of industrial manufacturing show that our method is able to significantly improve the performance of regression models on time series following previously unseen distributions.Graphic abstract Open image in new window
Reference:
Multi-Source Transfer Learning of Time Series in Cyclical Manufacturing (Werner Zellinger, Thomas Grubinger, Michael Zwick, Edwin Lughofer, Holger Schöner, Thomas Natschläger, Susanne Saminger-Platz), In Journal of Intelligent Manufacturing, 2019.
Bibtex Entry:
@article{ZellingerMultisourcetransferlearning2019,
title = {Multi-Source Transfer Learning of Time Series in Cyclical Manufacturing},
issn = {1572-8145},
doi = {10/ggbhdz},
abstract = {This paper describes a new transfer learning method for modeling sensor time series following multiple different distributions, e.g. originating from multiple different tool settings. The method aims at removing distribution specific information before the modeling of the individual time series takes place. This is done by mapping the data to a new space such that the representations of different distributions are aligned. Domain knowledge is incorporated by means of corresponding parameters, e.g. physical dimensions of tool settings. Results on a real-world problem of industrial manufacturing show that our method is able to significantly improve the performance of regression models on time series following previously unseen distributions.Graphic abstract Open image in new window},
language = {en},
urldate = {2019-10-21},
journal = {Journal of Intelligent Manufacturing},
url = {https://doi.org/10.1007/s10845-019-01499-4},
author = {Zellinger, Werner and Grubinger, Thomas and Zwick, Michael and Lughofer, Edwin and Schöner, Holger and Natschläger, Thomas and {Saminger-Platz}, Susanne},
month = oct,
year = {2019},
keywords = {Domain adaptation,Domain generalization,Multi-source transfer learning,Regression,Transfer learning},
file = {download/publications/Zellinger2019_MultiSourceTransferLearningTimeSeries_Springer_DOI_10.1007_s10845-019-01499-4.pdf}
}