@inproceedings{78e1d545cf6e4d03ba4f574e1e607b54,
title = "Data Augmentation for Industrial Prognosis Using Generative Adversarial Networks",
abstract = "The Industry 4.0 revolution allows monitoring and intelligent processing of big amounts of data. When monitoring certain assets, very few data is found for operation under faulty conditions because the cost of not operating properly is unacceptable and thus preventive strategies are put in practice. Because machine learning algorithms are data exhaustive, synthetic data can be created for these cases. Deep learning techniques have been proven to work very well for these cases. Generative Adversarial Networks (GANs) have been deployed in numerous applications with data augmentation objectives, but not so much for balancing unidimensional series with few data. In this paper, a GAN is applied in order to augment data for assets operating under faulty conditions. The proposed method is validated on a real industrial case, yielding promising results with respect to the case with no strategy for class imbalance whatsoever.",
keywords = "Generative Adversarial Networks, Data augmentation, Imbalanced data, Deep learning, Generative Adversarial Networks, Data augmentation, Imbalanced data, Deep learning",
author = "Patxi Ortego and Alberto Diez-Olivan and \{Del Ser\}, Javier and Basilio Sierra",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference date: 04-11-2020 Through 06-11-2020",
year = "2020",
month = oct,
day = "27",
doi = "10.1007/978-3-030-62365-4\_11",
language = "English",
isbn = "978-3-030-62365-4; 978-3-030-62364-7",
volume = "12490",
series = "0302-9743",
publisher = "Springer",
pages = "113--122",
editor = "Cesar Analide and Paulo Novais and David Camacho and Hujun Yin",
booktitle = "unknown",
address = "Germany",
}
