Rotor broken bar diagnostics in induction motor drive using Wavelet packet transform and ANFIS classification

Haitham Abu-Rub*, Atif Iqbal, Sk Moin Ahmed, Jaroslaw Guzinski, Marek Adamowicz, Mina Rahiminia

*Corresponding author for this work

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

10 Citations (Scopus)

Abstract

The paper proposes diagnostic technique for identifying rotor broken bar in speed sensorless three-phase induction motor drive system. Experimental data is collected for healthy and faulty motor with two rotor broken bar. The collected data is first processed using Wavelet packet 1-D transformation to extract the statistical information. The statistical information thus obtained is used to train the Adaptive Neuro-Fuzzy inference system (ANFIS) which is used as fault classifier. The stator current space vector magnitude is used in conjunction with Wavelet transform as the current signature is the most effective method of fault diagnosis. The speed signal obtained from the observer system is directly used in ANFIS. The proposed synergy of Wavelet packet and ANFIS provide highly accurate and computationally efficient tool that can be used for on-line fault diagnosis.

Original languageEnglish
Title of host publication2011 IEEE International Electric Machines and Drives Conference, IEMDC 2011
Pages365-370
Number of pages6
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE International Electric Machines and Drives Conference, IEMDC 2011 - Niagara Falls, ON, Canada
Duration: 15 May 201118 May 2011

Publication series

Name2011 IEEE International Electric Machines and Drives Conference, IEMDC 2011

Conference

Conference2011 IEEE International Electric Machines and Drives Conference, IEMDC 2011
Country/TerritoryCanada
CityNiagara Falls, ON
Period15/05/1118/05/11

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