TY - CONF
T1 - Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals
AU - Medina, Ruben
AU - Cerrada, Mariela
AU - Cabrera, Diego
AU - Sánchez, Rene Vinicio
AU - Li, Chuan
AU - De Oliveira, Jose Valente
PY - 2019/7/12
Y1 - 2019/7/12
N2 - 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.
AB - 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.
KW - acoustic emission
KW - deep learning
KW - Faults detection
KW - gearbox
KW - long short term memory networks
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85070519509&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85070519509&origin=inward
UR - http://www.mendeley.com/research/deep-learningbased-gear-pitting-severity-assessment-using-acoustic-emission-vibration-currents-signa
U2 - 10.1109/PHM-Paris.2019.00042
DO - 10.1109/PHM-Paris.2019.00042
M3 - Paper
SP - 210
EP - 216
T2 - Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019
Y2 - 1 May 2019
ER -