Multi-source transfer learning of time series in cyclical manufacturing
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 take...
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| Veröffentlicht in: | Journal of intelligent manufacturing 2020-03, Vol.31 (3), p.777-787 |
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| container_title | Journal of intelligent manufacturing |
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| creator | Zellinger, Werner Grubinger, Thomas Zwick, Michael Lughofer, Edwin Schöner, Holger Natschläger, Thomas Saminger-Platz, Susanne |
| description | 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.
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| doi_str_mv | 10.1007/s10845-019-01499-4 |
| format | Article |
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| subjects | Business and Management Control Learning Machines Manufacturing Mapping Mechatronics Modelling Performance enhancement Processes Production Regression models Robotics Time series |
| title | Multi-source transfer learning of time series in cyclical manufacturing |
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