Stator fault identification analysis in induction motor using multinomial logistic regression

Wara Pramesti, Ika Damayanti, Dimas Anton Asfani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

This paper is proposed a new method for identify stator fault in induction motor based on multinomial logistic regression analysis. A wavelet transform is used to calculate the value of high-frequency signals of motor electric current. The value of high frequency signal is then used as input variable of logistic regression to obtain the classification of the operating conditions that divided into a normal operation and symptom of damage. Three input variables (x1, X2, X3) which have been tested individually for modeling to identify the existence of fault. Those variables are obtained from three consecutive time period of current signal. Each period is 10ms. There is one input variable is X3 that no significant effect on the response variable, so that the simultaneous modeling of the variable is not included. Based on two input variables (x1 and X2) which are significantly affect response variables obtained, classification accuracy of stator fault identification is 77.5%.

Original languageEnglish
Title of host publicationProceeding - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016
Subtitle of host publicationRecent Trends in Intelligent Computational Technologies for Sustainable Energy
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages439-442
Number of pages4
ISBN (Electronic)9781509017096
DOIs
Publication statusPublished - 20 Jan 2017
Event2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016 - Lombok, Indonesia
Duration: 28 Jul 201630 Jul 2016

Publication series

NameProceeding - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016: Recent Trends in Intelligent Computational Technologies for Sustainable Energy

Conference

Conference2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016
Country/TerritoryIndonesia
CityLombok
Period28/07/1630/07/16

Keywords

  • Classification
  • Identification
  • Multinomial Logistic Regression
  • Stator Fault

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