Oil whirl fault detection in induction motors using orbital analysis and neural networks

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Fault detection in induction motors is a useful practice when some critical processes depend on good machines performance. This work proposes a new computational model for detecting oil whirl faults in induction motors using orbital patterns. Signal vibrations are measured and pre-processed in order to obtain a characteristic orbit that represents the motor condition where an oil whirl fault is present. Through an artificial neural network, the orbital patterns are classified according to the motor condition: good or faulty. Experimental results show a good performance for the proposed model, providing a new tool for recognizing problems in induction motors.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
PublisherSpringer
Pages286-296
Number of pages11
DOIs
StatePublished - 1 Jan 2018

Publication series

NameLecture Notes in Networks and Systems
Volume15
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Keywords

  • Fault
  • Induction motor
  • Oil whirl
  • Pattern processing

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