Influence of Accelerometer Position on Gearbox Fault Severity Classification through Evaluation of Deep Learning Models

Jean Carlo MacAncela, Diego Cabrera, Pablo Lucero, Mariela Cerrada, Chuan Li, Sergio Villacrés, Réne Vinicio Sánchez

Research output: Contribution to conferenceChapter

Abstract

Gears are key elements in mechanical transmission systems. Fault diagnosis in gearboxes is a cutting-edge topic nowadays mainly addressed by machine learning approaches. The success classifying a fault under this approach depends directly on the quality of the information provided to the models, and in gearboxes, quality of captured information depends on the place where a sensor is located. In this work, we propose a deep learning approach for the evaluation of the best of two accelerometers positions for classifying nine severity levels in gearboxes. Based on the performance of LSTM models whose hyperparameters have been found by a Bayesian optimization, we show which one is the best source of information for this layout. Also, we have performed statistical comparisons in order to find any statistical differences between models and accelerometers.

Original languageEnglish
Pages303-308
Number of pages6
DOIs
StatePublished - 12 Jul 2019
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

  • Bayesian Optimization
  • Fault severity classification
  • hyperparameters search
  • K-S test
  • LSTM networks

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