Stator winding short-circuit fault diagnosis in induction motors using random forest

Tiago Dos Santos, Fernando J.T.E. Ferreira, Joao Moura Pires, Carlos Damasio

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

7 Citations (Scopus)

Abstract

In this paper, an approach to detect stator winding short-circuit faults in squirrel-cage induction motors based on Random Forest and Park's Vector is proposed. This is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park's Vector, both for current and voltage. To score the unbalance in the d-q space, a Principal Component Analysis is applied to Park's Vector and with the first two principal components the eccentricity is calculated, while the first principal component is used to determine the phase in short-circuit. The proposed strategy has been experimentally tested on a special 400-V, 50-Hz, 4-pole, 2.2-kW induction motor with reconfigurable stator windings in which it was possible to emulate different types of inter-Turn short-circuits. The results are quite promising, even only using 1-kHz sampling frequency to acquire the current and voltage waveforms in the three phases, and the use of the Fast Fourier Transform is avoided. The developed solution may be used for tele-monitoring of the motor condition and to implement advanced predictive maintenance strategies.

Original languageEnglish
Title of host publication2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)978-1-5090-4281-4
DOIs
Publication statusPublished - 3 Aug 2017
Event2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017 - Miami, United States
Duration: 21 May 201724 May 2017

Conference

Conference2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017
CountryUnited States
CityMiami
Period21/05/1724/05/17

Keywords

  • classification
  • fault diagnosis
  • induction motor
  • inter-Turn short-circuit
  • machine learning
  • PCA
  • predictive maintenance
  • random forest
  • stator winding

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  • Cite this

    Dos Santos, T., Ferreira, F. J. T. E., Pires, J. M., & Damasio, C. (2017). Stator winding short-circuit fault diagnosis in induction motors using random forest. In 2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017 [8002350] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEMDC.2017.8002350