2022/01/11 by Luca Strazzera, V. Gori, Valentina Gori +4
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #cs.LG
paper · pdf · doi:10.48550/arxiv.2201.03850
5 pages, 3 figures, NeurIPS 2021
arxiv created 2022/01/11 · openalex publication_date 2022/01/11 · arxiv updated 2022/01/12 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
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.