Towards Advanced Diagnosis Recognition for Eccentricities Faults: Application on Induction Motor

I. Bouchareb, A. Lebaroud, A. J.M. Cardoso, S. Bin Lee

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

Abstract

Artificial Intelligence (AI) is expected to be a large driver in industrial applications competitiveness in the not-so-distant future. Induction motors (IMs) are used worldwide as the 'workhorse' in industrial applications. The paper reviews the possibility of integrating artificial intelligence techniques for condition monitoring and fault diagnosis of induction motors so-called advanced diagnosis. The paper focuses on advanced diagnosis method related on the recognition, classification and prognostics of eccentricities faults in induction motor drives. Rotor eccentricity has been the aim of many researchers. However reliably detection and accurate prediction of eccentricity fault is still not possible and difficult task if appear individually. To face this situation, an intelligent diagnosis system merges Neural Network and Hidden Markov Model together (NN-HMM) into a common framework to overcome the deficiencies of eccentricity diagnosis. Current measurements based on non-parametrical Time-Frequency Representation (TFR) are used for features extraction. Then, a features selection method using Fisher's Discriminant Ratio (FDR) is applied to select an optimal number of the extracted features associated with polynomial approach to track, recognize of various eccentricities faults types and degree precisely. An experimental study on a 7.5h induction motor prove the reliability and the efficiency of the proposed method in condition monitoring of eccentricities with different degree 0%, 20%, 40%, 60, 80% precisely independent of load or motor type.

Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages271-282
Number of pages12
ISBN (Electronic)9781728118321
DOIs
Publication statusPublished - 2019 Aug
Externally publishedYes
Event12th IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2019 - Toulouse, France
Duration: 2019 Aug 272019 Aug 30

Publication series

NameProceedings of the 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2019

Conference

Conference12th IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2019
CountryFrance
CityToulouse
Period19/8/2719/8/30

Keywords

  • Advanced diagnosis
  • artificial intelligence
  • detection
  • eccentricity faults
  • Fisher's kernel
  • Hidden Markov Model
  • induction motor
  • Neural Network
  • non-parametrical Time-Frequency Representation (TFR)
  • polynomial approach

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Bouchareb, I., Lebaroud, A., Cardoso, A. J. M., & Lee, S. B. (2019). Towards Advanced Diagnosis Recognition for Eccentricities Faults: Application on Induction Motor. In Proceedings of the 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2019 (pp. 271-282). [8864920] (Proceedings of the 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DEMPED.2019.8864920