Multi-source transfer learning of time series in cyclical manufacturing

Werner Zellinger (), Thomas Grubinger (), Michael Zwick (), Edwin Lughofer (), Holger Schöner (), Thomas Natschläger () and Susanne Saminger-Platz ()
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Werner Zellinger: Johannes Kepler University Linz
Thomas Grubinger: Software Competence Center Hagenberg GmbH
Michael Zwick: Software Competence Center Hagenberg GmbH
Edwin Lughofer: Johannes Kepler University Linz
Holger Schöner: Software Competence Center Hagenberg GmbH
Thomas Natschläger: Software Competence Center Hagenberg GmbH
Susanne Saminger-Platz: Johannes Kepler University Linz

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 3, No 14, 777-787

Abstract: 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

Keywords: Transfer learning; Multi-source transfer learning; Regression; Domain generalization; Domain adaptation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-019-01499-4

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