Artículo
One-shot fault diagnosis of 3D printers through improved feature space learning
Autor/es | Li, Chuan
Cabrera, Diego Sancho Caparrini, Fernando Sánchez, René-Vinicio Cerrada, Mariela Oliveira, José Valente de |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2020 |
Fecha de depósito | 2021-04-19 |
Publicado en |
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Resumen | Signal acquisition from mechanical systems working in faulty conditions is normally expensive. As a consequence, supervised learning-based approaches are hardly applicable. To address this problem, a one-shot learning-based ... Signal acquisition from mechanical systems working in faulty conditions is normally expensive. As a consequence, supervised learning-based approaches are hardly applicable. To address this problem, a one-shot learning-based approach is proposed for multi-class classification of signals coming from a feature space created only from healthy condition signals and one single sample for each faulty class. First, a transformation mapping between the input signal space and a feature space is learned through a bidirectional generative adversarial network. Next, the identification of different health condition regions in this feature space is carried out by means of a single input signal per fault. The method is applied to three fault diagnosis problems of a 3D printer and outperforms other methods in the literature. |
Cita | Li, C., Cabrera, D., Sancho Caparrini, F., Sánchez, R., Cerrada, M. y Oliveira, J.V.d. (2020). One-shot fault diagnosis of 3D printers through improved feature space learning. IEEE Transactions on Industrial Electronics |
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