Abstract

This paper is proposing Naïve Bayes classifier detection system to identify the symptom of stator winding deterioration. The proposed system is based on probabilistic classifier with strong independence assumption of each fault case. The temporary short circuit case is defined as non permanent short circuit fault with high impedance. This fault case is representing the early stage of stator insulation break down. The laboratory experiment is performed to simulate the fault cases consist of induction motor with stator modification and current measurement system. The detection system is trained to identify the temporary short circuit occurrence consist of transient starting, steady state and ending of temporary short circuit. The system is also tested using non trained data to clarify the detection performance.

Original languageEnglish
Title of host publicationProceedings - 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2013
PublisherIEEE Computer Society
Pages288-294
Number of pages7
ISBN (Print)9781479900251
DOIs
Publication statusPublished - 2013
Event2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2013 - Valencia, Spain
Duration: 27 Aug 201330 Aug 2013

Publication series

NameProceedings - 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2013

Conference

Conference2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2013
Country/TerritorySpain
CityValencia
Period27/08/1330/08/13

Keywords

  • Fault detection
  • Wavelet transforms
  • bayesian methods
  • induction motor
  • kernel
  • stators

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