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Vibration Analysis in Turbomachines Using Machine Learning Techniques

Author

Listed:
  • Allan Alves Pinheiro
  • Iago Modesto Brandao
  • Cesar Da Costa

Abstract

This study proposes a method for diagnosing faults in turbomachines using machine learning techniques. In this study, a support vector machine-SVM algorithm is proposed for fault diagnosis of rotor rotation imbalance. Recently, support vector machines (SVMs) have become one of the most popular classification methods in vibration analysis technology. Axis unbalance defect is classified using support vector machines. The experimental data is derived from the turbomachine model of the rigid-shaft rotor and the flexible bearings, and the experimental setup for vibration analysis. Several situations of unbalance defects have been successfully detected.

Suggested Citation

  • Allan Alves Pinheiro & Iago Modesto Brandao & Cesar Da Costa, 2019. "Vibration Analysis in Turbomachines Using Machine Learning Techniques," European Journal of Engineering and Technology Research, European Open Science, vol. 4(2), pages 12-16, February.
    • Handle: RePEc:epw:ejeng0:v:4:y:2019:i:2:id:61128
    DOI: 10.24018/ejeng.2019.4.2.1128
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    References listed on IDEAS

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    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
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