DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift

• URL: http://arxiv.org/abs/2201.03850v1
• Date: Tue, 11 Jan 2022 09:24:33 GMT
• Title: DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift
• Authors: Luca Strazzera and Valentina Gori and Giacomo Veneri
• Abstract 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. We report a significant improvement in regression performance, compared to the baseline model trained on the source domain only.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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