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Publicação
Deep Learning-Based Gear Pitting Severity Assessment using Acoustic Emission, Vibration and Currents signals
Orientador(es)
Resumo(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.
Descrição
Palavras-chave
Fault-Diagnosis Frequency
Contexto Educativo
Citação
Editora
IEEE