Artículo
Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery
Autor/es | Cabrera, Diego
Sancho Caparrini, Fernando Long, Jianyu Sánchez, René-Vinicio Zhang, Shaohui Cerrada, Mariela Li, Chuan |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2019 |
Fecha de depósito | 2020-04-07 |
Publicado en |
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Resumen | At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is in equal proportions for all conditions. However, when the application ... At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is in equal proportions for all conditions. However, when the application is closer to reality, the problem of data imbalance is increasingly evident. In this paper, we propose a method for the creation of diagnoses that consider an extreme imbalance in the available data. Our approach first processes the vibration signals of the machine using a wavelet packet transform-based feature-extraction stage. Then, improved generative models are obtained with a dissimilarity-based model selection to artificially balance the dataset. Finally, a Random Forest classifier is created to address the diagnostic task. This methodology provides a considerable improvement with 99% of data imbalance over other approaches reported in the literature, showing performance similar to that obtained with a balanced set of data. |
Agencias financiadoras | National Natural Science Foundation of China |
Identificador del proyecto | 51605406
71801046 2016KZDXM054, 2016YFE0132200 |
Cita | Cabrera, D., Sancho Caparrini, F., Long, J., Sánchez, R., Zhang, S., Cerrada, M. y Li, C. (2019). Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery. IEEE Access, 7, 70643-70653. |
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