Advanced analysis of motor currents for the diagnosis of the rotor condition in electric motors operating in mining facilities

Research output: Contribution to journalArticle

Researchers

  • Jose Antonino-Daviu
  • Alfredo Quijano-Lopez
  • Martin Rubbiolo
  • Vicente Climente-Alarcon

Research units

  • Polytechnic University of Valencia
  • MYG Motores y Generadores Inc.

Abstract

Predictive maintenance of electric motors is a topic of increasing importance in many industrial applications. The mining industry is not an exception; many electric motors operating in mining facilities are critical machines and their unexpected failures may imply significant losses and can be hazardous for the users. Due to these facts, an increasing research effort has been dedicated to investigate on new techniques that are able to provide a reliable diagnostic of the motor condition. Over recent years, monitoring of electrical quantities (e.g. motor currents) has emerged as a very attractive option for determining the health of several motor parts (rotor, eccentricities, bearings) due to its very interesting advantages: possibility of remote motor monitoring, non-invasive nature, simple application, broad fault coverage. The traditional methods based on analysis of motor currents during steady-state operation (MCSA) are being complemented, when not replaced, by more reliable approaches. This work applies an innovative transient based methodology (ATCSA) to several case studies referred to large motors operating in mining facilities. The results prove how this modern methodology enables to overcome some important drawbacks of the classical MCSA, such as its unsuitability under varying speed conditions, and may provide an earlier indication of rotor electrical asymmetries under such working conditions.

Details

Original languageEnglish
Pages (from-to)3934-3942
Number of pages9
JournalIEEE Transactions on Industry Applications
Volume54
Issue number4
Early online date22 Mar 2018
Publication statusPublished - Jul 2018
MoE publication typeA1 Journal article-refereed

    Research areas

  • fault detection, fault diagnosis, Induction motors, mining, reliability, rotor, transient analysis, wavelet

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