ANN-based for detection, diagnosis the bearing fault for three phase induction motors using current signal

Shady Khalil, Haitham Abu-Rub, M. S. Saad, E. M. Aboul-Zahab, Atif Iqbal

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

27 Citations (Scopus)

Abstract

This paper develop a novel, non-intrusive approach for fault-detection and diagnosis scheme of bearing faults for three-phase induction motor using stator current signals with particular interest in identifying the outer-race defect at an early stage. The most common bearing problem is the outer race defect in the load zone. The empirical mode decomposition (EMD) technique is proposed for analysis of non-stationary stator current signals. The stator current signal is decomposed in intrinsic mode function (IMF) using empirical mode decomposition. The extracted IMFs apply on the wigner-ville distribution (WVD) to have the contour pattern of WVD. Then, artificial neural network is used for pattern recognition that can effectively detect outer-race defects of bearing. The experimental results show that stator current-based monitoring with winger-ville distribution based on EMD yields a high degree of accuracy in fault detection and diagnosis of outer-race defects at different load conditions, also, a more significant and reliable indicator for detection and diagnosis of outer-race defects using artificial neural network. Experimental investigation is done and reported in the paper.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Industrial Technology, ICIT 2013
Pages253-258
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Conference on Industrial Technology, ICIT 2013 - Cape Town, South Africa
Duration: 25 Feb 201328 Feb 2013

Other

Other2013 IEEE International Conference on Industrial Technology, ICIT 2013
CountrySouth Africa
CityCape Town
Period25/2/1328/2/13

Fingerprint

Bearings (structural)
Induction motors
Stators
Defects
Wigner-Ville distribution
Decomposition
Fault detection
Failure analysis
Neural networks
Pattern recognition
Monitoring

Keywords

  • Bearing fault
  • Fault detection
  • hilbert huang
  • Incipient Fault
  • neural networks
  • wigner ville

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Khalil, S., Abu-Rub, H., Saad, M. S., Aboul-Zahab, E. M., & Iqbal, A. (2013). ANN-based for detection, diagnosis the bearing fault for three phase induction motors using current signal. In Proceedings - 2013 IEEE International Conference on Industrial Technology, ICIT 2013 (pp. 253-258). [6505681] https://doi.org/10.1109/ICIT.2013.6505681

ANN-based for detection, diagnosis the bearing fault for three phase induction motors using current signal. / Khalil, Shady; Abu-Rub, Haitham; Saad, M. S.; Aboul-Zahab, E. M.; Iqbal, Atif.

Proceedings - 2013 IEEE International Conference on Industrial Technology, ICIT 2013. 2013. p. 253-258 6505681.

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

Khalil, S, Abu-Rub, H, Saad, MS, Aboul-Zahab, EM & Iqbal, A 2013, ANN-based for detection, diagnosis the bearing fault for three phase induction motors using current signal. in Proceedings - 2013 IEEE International Conference on Industrial Technology, ICIT 2013., 6505681, pp. 253-258, 2013 IEEE International Conference on Industrial Technology, ICIT 2013, Cape Town, South Africa, 25/2/13. https://doi.org/10.1109/ICIT.2013.6505681
Khalil S, Abu-Rub H, Saad MS, Aboul-Zahab EM, Iqbal A. ANN-based for detection, diagnosis the bearing fault for three phase induction motors using current signal. In Proceedings - 2013 IEEE International Conference on Industrial Technology, ICIT 2013. 2013. p. 253-258. 6505681 https://doi.org/10.1109/ICIT.2013.6505681
Khalil, Shady ; Abu-Rub, Haitham ; Saad, M. S. ; Aboul-Zahab, E. M. ; Iqbal, Atif. / ANN-based for detection, diagnosis the bearing fault for three phase induction motors using current signal. Proceedings - 2013 IEEE International Conference on Industrial Technology, ICIT 2013. 2013. pp. 253-258
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