Application of Different Machine Learning Strategies for Current- And Vibration-based Motor Bearing Fault Detection in Induction Motors

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1 Citation (Scopus)

Abstract

In this paper, the application of different machine learning strategies for current- and vibration-based detection of bearing faults in squirrel-cage induction motors is studied. This study compares several feature extraction strategies such as a statistical and spectral analysis of vibration, a statistical analysis of the Hilbert's Transform envelope of vibration, an analysis of the currents deviation to a perfect sinusoid and a statistical and spectral analysis of the Park's Vector Modulus, with its performances being evaluated with the Support Vector Machine, Artificial Neural Network, Random Forests and Extreme Gradient Boosting algorithms. A comparison of results obtained using sampling frequencies of 0.8 kHz, 1 kHz, 2 kHz, 5 kHz and 10 kHz and analysis periods between 20 ms and 100 ms is made and promising models are achieved even with the lowest sampling frequencies.

Original languageEnglish
Title of host publicationProceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
Pages68-73
Number of pages6
ISBN (Electronic)9781728148786
DOIs
Publication statusPublished - Oct 2019
Event45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal
Duration: 14 Oct 201917 Oct 2019

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
PublisherIEEE Computer Society
Volume2019-October
ISSN (Print)1553-572X

Conference

Conference45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
CountryPortugal
CityLisbon
Period14/10/1917/10/19

Keywords

  • bearing fault detection
  • condition monitoring
  • current analysis
  • induction motor
  • machine learning
  • predictive maintenance
  • vibration analysis

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