Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals

Ruben Medina, Mariela Cerrada, Diego Cabrera, Rene Vinicio Sánchez, Chuan Li, Jose Valente De Oliveira

Research output: Contribution to conferencePaper

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.

Original languageEnglish
Pages210-216
Number of pages7
DOIs
StatePublished - 12 Jul 2019
Externally publishedYes
EventProceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019 -
Duration: 1 May 2019 → …

Conference

ConferenceProceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019
Period1/05/19 → …

Keywords

  • acoustic emission
  • deep learning
  • Faults detection
  • gearbox
  • long short term memory networks

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