Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signals
Collecting data from mechanical systems in abnormal conditions is expensive and time consuming. Consequently, fault detection approaches based on classical supervised learning working with both normal and abnormal data are not applicable in some condition-based maintenance tasks. To address this pro...
| Autor principal: | |
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| Outros Autores: | , , , , , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2021
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.1/17056 |
| País: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/17056 |