Self Organizing Map (SOM) approach for classification of mechanical faults in induction motors


9th International Work-Conference on Artificial Neural Networks, San Sebastian, Spain, 20 - 22 June 2007, vol.4507, pp.855-856 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 4507
  • Doi Number: 10.1007/978-3-540-73007-1_103
  • City: San Sebastian
  • Country: Spain
  • Page Numbers: pp.855-856
  • Anadolu University Affiliated: Yes


In this work, Self Organizing Map (SOM) is used in order to detect and classify the broken rotor bars and misalignment type mechanical faults that often occur in induction motors which are widely used in industry. The feature vector samples are extracted from the sampled line current of motors with fault and healthy one. These samples are the poles of the AR model which is obtained from the spectrum of sampled line current. The waveforms are obtained from four different 3 hp test motors. Two of them have different number of broken rotor bars, one test motor has misalignment problem and the last one is the healthy motor. Broken rotor bar and misalignment faults are successfully classified and distinguished from the healthy motor using SOM classification with the feature vectors. It is also worth to mention that discrimination of different number of broken rotor bars has been achieved.