Deep Learning-Based Gear Pitting Severity Assessment using Acoustic Emission, Vibration and Currents signals

Medina, Ruben; Cerrada, Mariela; Cabrera, Diego; Sanchez, Rene-Vinicio; Li, Chuan; Valente de Oliveira, José

http://hdl.handle.net/10400.1/14460

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Authors

Medina, Ruben

Cerrada, Mariela

Cabrera, Diego

Sanchez, Rene-Vinicio

Li, Chuan

Valente de Oliveira, José

Abstract(s)

A 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.