Severity estimation of stator winding short-circuit faults using cubist

Tiago dos Santos, Fernando J.T.E. Ferreira, João Moura Pires, Carlos Viegas Damásio

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

Abstract

In this paper, an approach to estimate the severity of stator winding short-circuit faults in squirrel-cage induction motors based on the Cubist model 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. The proposed method presents a systematic comparison between models, as well as an analysis regarding hyper-parameter tunning, where the novelty of the presented work is mainly associated with the application of data-based analysis techniques to estimate the stator winding short-circuit severity in three-phase squirrel-cage induction motors. 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 publicationProgress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Proceedings
PublisherSpringer Verlag
Pages217-228
Number of pages12
Volume10423 LNAI
ISBN (Electronic)978-3-319-65340-2
ISBN (Print)978-3-319-65339-6
DOIs
Publication statusPublished - 1 Jan 2017
Event18th EPIA Conference on Artificial Intelligence, EPIA 2017 - Porto, Portugal
Duration: 5 Sep 20178 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10423 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th EPIA Conference on Artificial Intelligence, EPIA 2017
CountryPortugal
CityPorto
Period5/09/178/09/17

Keywords

  • Cubist
  • Fault diagnosis
  • Induction motor
  • Inter-turn short-circuit
  • Machine learning
  • Regression
  • Severity estimation

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  • Cite this

    dos Santos, T., Ferreira, F. J. T. E., Pires, J. M., & Damásio, C. V. (2017). Severity estimation of stator winding short-circuit faults using cubist. In Progress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Proceedings (Vol. 10423 LNAI, pp. 217-228). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10423 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-65340-2_18