DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift
We propose an adversarial learning method to tackle a Domain Adaptation (DA) time series regression task (DANNTe). The regression aims at building a virtual copy of a sensor installed on a gas turbine, to be used in place of the physical sensor which can be missing in certain situations. Our DA appr...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
11.01.2022
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Subjects | |
Online Access | Get full text |
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Summary: | We propose an adversarial learning method to tackle a Domain Adaptation (DA)
time series regression task (DANNTe). The regression aims at building a virtual
copy of a sensor installed on a gas turbine, to be used in place of the
physical sensor which can be missing in certain situations. Our DA approach is
to search for a domain-invariant representation of the features. The learner
has access to both a labelled source dataset and an unlabeled target dataset
(unsupervised DA) and is trained on both, exploiting the minmax game between a
task regressor and a domain classifier Neural Networks. Both models share the
same feature representation, learnt by a feature extractor. This work is based
on the results published by Ganin et al. arXiv:1505.07818; indeed, we present
an extension suitable to time series applications. We report a significant
improvement in regression performance, compared to the baseline model trained
on the source domain only. |
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DOI: | 10.48550/arxiv.2201.03850 |