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

Medina, RubenCerrada, MarielaCabrera, DiegoSanchez, Rene-VinicioLi, ChuanValente de Oliveira, José2020-07-242020-07-242019978-1-7281-0329-72166-5656http://hdl.handle.net/10400.1/14460A 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.engFault-DiagnosisFrequencyDeep Learning-Based Gear Pitting Severity Assessment using Acoustic Emission, Vibration and Currents signalsconference object10.1109/PHM-Paris.2019.00042