Diagnoses of internal faults of three phase squirrel cage induction motor - A review

Research output: Chapter in Book/Report/Conference proceedingConference contribution

68 Citations (Scopus)

Abstract

Three phase squirrel cage induction motors are the most popular motors in industries. Electrical, magnetic, mechanical, thermal and environmental stresses during operating conditions lead to internal faults in it. There are no reliable non-invasive tools available for early diagnoses of internal faults. Hence, these internal faults are likely to be left undetected in its early stage, leading to unscheduled maintenance, process shutdown and huge financial loss in industries. Early detection of faults helps to save resources by avoiding process shutdown/repair of machines. Hence, there is a need for a reliable non-invasive condition monitoring system for three phase squirrel cage induction motors. Condition monitoring involves non-invasive acquisition of signals, processing, fault signature extraction, decision making on the presence and type of faults. This paper reviews the current trends in internal fault diagnosis of induction machines and identifies future research options. A statistical analysis of the results of motor current signatures obtained from a motor with rotor bar faults is done to study the relative effect of fault severity and load variation on the current signature.

Original languageEnglish
Title of host publicationProceedings of the 2014 International Conference on Advances in Energy Conversion Technologies - Intelligent Energy Management
Subtitle of host publicationTechnologies and Challenges, ICAECT 2014
PublisherIEEE Computer Society
Pages48-54
Number of pages7
DOIs
Publication statusPublished - 2014
Event2014 International Conference on Advances in Energy Conversion Technologies - Intelligent Energy Management: Technologies and Challenges, ICAECT 2014 - Manipal, India
Duration: 23-01-201425-01-2014

Conference

Conference2014 International Conference on Advances in Energy Conversion Technologies - Intelligent Energy Management: Technologies and Challenges, ICAECT 2014
Country/TerritoryIndia
CityManipal
Period23-01-1425-01-14

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Fuel Technology

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