Browsing by Author "Medina, Ruben"
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- Deep Learning-Based Gear Pitting Severity Assessment using Acoustic Emission, Vibration and Currents signalsPublication . Medina, Ruben; Cerrada, Mariela; Cabrera, Diego; Sanchez, Rene-Vinicio; Li, Chuan; Valente de Oliveira, JoséA method for gearbox pitting faults severity classification using Deep Learning techniques is reported. The signals are preprocessed for obtaining a 2D time-frequency representation corresponding to the Mel Frequency Cepstral Coefficients. This bi-dimensional representation is the feature space used for classification. A Long Short Term Memory network (LSTM) is used for classifying nine levels of pitting in spur gears. Each signals dataset is used for training and validating a LSTM network. Classification accuracies up to 100 % are obtained during cross-validation with the analyzed signals dataset.