Artículo
Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signals
Autor/es | Li, Chuan
Cabrera, Diego Sancho Caparrini, Fernando Sánchez, René-Vinicio Cerrada, Mariela Long, Jianyu Oliveira, José Valente de |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2021 |
Fecha de depósito | 2021-04-16 |
Publicado en |
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Resumen | 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 ... 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 conditionbased maintenance tasks. To address this problem, this paper proposes Fusing Convolutional Generative Adversarial Encoders (fCGAE) method to create fault detection models from only normal data. Firstly, to obtain an adequate deep feature space, encoder models based on 1D convolutional neural networks are created. Then, these encoders are optimized in an unsupervised way through Bidirectional Generative Adversarial Networks. Finally, the multi-channel features collected from the system are merged with One-Class Support Vector Machine. fCGAE is applied to fault detection in 3D printers, where experimental results in two fault detection cases show excellent generalization capabilities and better performance compared to peer methods. |
Cita | Li, C., Cabrera, D., Sancho Caparrini, F., Sánchez, R., Cerrada, M., Long, J. y Oliveira, J.V.d. (2021). Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signals. Mechanical Systems and Signal Processing, 147 (January 2021-107108) |
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