DANNTe: A Case Study of a Turbo-Machinery Sensor Virtualization Under Domain Shift

Abstract

We propose an adversarial learning method to tackle a Domain Adaptation time series regression task (DANNTe). The task concerns the virtualization of a physical sensor of a turbine with aim to build a reliable virtual sensor working on operating conditions not considered during the training phase. Our approach is directly inspired by the need to have a domain-invariant representation of the features to correct the covariate shift present in the data. The learner has access to both a labeled source data and unlabeled target data (Unsupervised DA) and is trained on both, exploiting the minmax game between a task regressor neural network and a domain classifier neural network. Both models share the same feature representation in terms of a feature extractor neural network. This work is based on the work of Ganin et al.; we present an extension suitable to be applied to time series data. The results report a significant improvement in regression performance, compared to the base model trained on the source domain only.