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Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning
2019
IEEE Access
This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis
doi:10.1109/access.2019.2934233
fatcat:sxhehpp5hnb57c7n5tkteybvbm