Publicação
Deep Learning-Based Gear Pitting Severity Assessment using Acoustic Emission, Vibration and Currents signals
| Resumo: | 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. |
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| Autores principais: | Medina, Ruben |
| Outros Autores: | Cerrada, Mariela; Cabrera, Diego; Sanchez, Rene-Vinicio; Li, Chuan; Valente de Oliveira, José |
| Assunto: | Fault-Diagnosis Frequency |
| Ano: | 2019 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Algarve |
| Idioma: | inglês |
| Origem: | Sapientia - Universidade do Algarve |
| Resumo: | 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. |
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