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.
CITATION STYLE
Medina, R., Cerrada, M., Cabrera, D., Sánchez, R. V., Li, C., & De Oliveira, J. V. (2019). Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals. In Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019 (pp. 210–216). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/PHM-Paris.2019.00042
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