Smart Structures and Systems

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Cracked rotor diagnosis by means of frequency spectrum and artificial neural networks

  • Munoz-Abella, B. (Department of Mechanical Engineering, University Carlos III of Madrid) ;
  • Ruiz-Fuentes, A. (Department of Mechanical Engineering, University Carlos III of Madrid) ;
  • Rubio, P. (Department of Mechanical Engineering, University Carlos III of Madrid) ;
  • Montero, L. (Department of Mechanical Engineering, University Carlos III of Madrid) ;
  • Rubio, L. (Department of Mechanical Engineering, University Carlos III of Madrid)
  • Received : 2019.05.23
  • Accepted : 2019.08.12
  • Published : 2020.04.25
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Abstract

The presence of cracks in mechanical components is a very important problem that, if it is not detected on time, can lead to high economic costs and serious personal injuries. This work presents a methodology focused on identifying cracks in unbalanced rotors, which are some of the most frequent mechanical elements in industry. The proposed method is based on Artificial Neural Networks that give a solution to the presented inverse problem. They allow to estimate unknown crack parameters, specifically, the crack depth and the eccentricity angle, depending on the dynamic behavior of the rotor. The necessary data to train the developed Artificial Neural Network have been obtained from the frequency spectrum of the displacements of the well- known cracked Jeffcott rotor model, which takes into account the crack breathing mechanism during a shaft rotation. The proposed method is applicable to any rotating machine and it could contribute to establish adequate maintenance plans.

Keywords

Acknowledgement

Supported by : Spanish Ministerio de Economia y Competitividad

The authors would like to thank the Spanish Ministerio de Economiay Competitividad for the support for this work through the projects DPI2009-13264 and DPI2013-45406-P.

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