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    Data Augmentation for Industrial Prognosis Using Generative Adversarial Networks

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    Ortego2020_Chapter_D ... (2.459Mb)
    Identifiers
    URI: http://hdl.handle.net/11556/1035
    ISSN: 0302-9743
    ISBN: 978-3-030-62365-4; 978-3-030-62364-7
    DOI: 10.1007/978-3-030-62365-4_11
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    Author/s
    Ortego, Patxi; Diez-Olivan, Alberto; Del Ser, Javier; Sierra, Basilio
    Date
    2020-10-27
    Keywords
    Generative Adversarial Networks
    Data augmentation
    Imbalanced data
    Deep learning
    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.
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    conferenceObject

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