Data Augmentation for Industrial Prognosis Using Generative Adversarial Networks

Ortego, Patxi; Diez-Olivan, Alberto; Del Ser, Javier; Sierra, Basilio

Data Augmentation for Industrial Prognosis Using Generative Adversarial Networks

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Ortego2020_Chapter_DataAugmentationForIndustrialP.pdf (2.46 MB)

Identifiers

URI: http://hdl.handle.net/11556/1035

ISSN: 0302-9743

ESSN: 1611-3349

ISBN: 978-3-030-62365-4; 978-3-030-62364-7

DOI: 10.1007/978-3-030-62365-4_11

Publication date

2020-10-27

Authors

Publisher

Springer

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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

Type

conference output

Citation

Ortego, Patxi, Alberto Diez-Olivan, Javier Del Ser, and Basilio Sierra. “Data Augmentation for Industrial Prognosis Using Generative Adversarial Networks.” Intelligent Data Engineering and Automated Learning – IDEAL 2020 (2020): 113–122. doi:10.1007/978-3-030-62365-4_11.

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