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Deep learning-based gear pitting severity assessment using acoustic emission, vibration and currents signals

Abstract:

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.

Año de publicación:

2019

Keywords:

    Fuente:

    googlegoogle

    Tipo de documento:

    Other

    Estado:

    Acceso abierto

    Áreas de conocimiento:

    • Ingeniería mecánica
    • Aprendizaje automático

    Áreas temáticas:

    • Métodos informáticos especiales
    • Física aplicada
    • Ingeniería y operaciones afines

    Contribuidores:

    Diego Cabrera MendietaDiego Cabrera Mendieta