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Artículo
Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signals
(Elsevier, 2021)
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 ...
Artículo
Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation
(Elsevier, 2017)
Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features ...
Artículo
One-shot fault diagnosis of 3D printers through improved feature space learning
(IEEE Computer Society, 2020)
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 ...