Intelligent Systems Applied to the Classification of Multiple Faults in Inverter Fed Induction Motors

  • Wagner Fontes Godoy
  • Alessandro Goedtel
  • Ivan Nunes da Silva
  • Rodrigo Henrique Cunha Palácios
  • Alexandre L’Erario
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)


The monitoring condition of electrical machine is an important parameter for maintenance of industrial process operation levels. In this paper, an investigation based on learning machine classifiers to proper classify machine multiple faults i.e. stator short-circuits, broken rotor bars and bearings in three phase induction motors driven by different inverters models is proposed. Experimental tests were performed in 2 different motors, running at steady state, operating under variable speed and torque variation resulting in 2967 samples. The main concept of proposed approach is to apply the three phase current amplitudes to immediately detect motor operating conditions. The high dimensionality of the input vectors in the algorithms was solved through the discretization of the current data, which allows the reduction the classification complexity providing a optimized waveform in comparison with the original one. The results show that it is possible to classify accurately these faults.


Intelligent systems Three-phase induction motor Inverters Fault diagnosis 



The authors would like to thank the support and motivation provided by the Federal Technological University of Paraná.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Wagner Fontes Godoy
    • 1
  • Alessandro Goedtel
    • 1
  • Ivan Nunes da Silva
    • 2
  • Rodrigo Henrique Cunha Palácios
    • 1
  • Alexandre L’Erario
    • 1
  1. 1.Department of Electrical and Computer EngineeringFederal Technological University of ParanáCornélio ProcópioBrazil
  2. 2.São Carlos School of EngineeringUniversity of São PauloSão CarlosBrazil

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