One-shot fault diagnosis of 3D printers through improved feature space learning
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...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/107272 |
| Acceso en línea: | https://hdl.handle.net/11441/107272 https://doi.org/10.1109/TIE.2020.3013546 |
| Access Level: | acceso abierto |
| Palabra clave: | Deep learning Fault diagnosis One-shot learning 3D printer |
| Sumario: | 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. |
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