3-phase Induction Motor Bearing Fault Detection and Isolation using MCSA Technique Based on Neural Network Algorithm

المؤلفون

  • Nawal A. Hussein Electrical Engineering Department, College of Engineering, Al-Mustansiriyah University, Baghdad, Iraq مؤلف
  • Dhari Yousif Mahmood Electrical Engineering Department, College of Engineering, Al-Mustansiriyah University, Baghdad, Iraq مؤلف
  • Essam M. Abdul-Baki Electrical Engineering Department, College of Engineering, Al-Mustansiriyah University, Baghdad, Iraq مؤلف

الكلمات المفتاحية:

Induction motors، diagnosis، data acquisition، fault detection، modeling، and bearings fault

الملخص

This paper shows a system that has the ability to diagnose bearing fault in three phase induction motor by using Motor Current Signature Analysis (MCSA) technique associated with artificial neural network (ANN) algorithm. Mathematical models for healthy and faulty conditions built to demonstrate theoretically the behavior of 3-phase induction motor in both cases. The effects of such a fault on motor currents waveforms at different loads studied experimentally using practical data acquisition and Fast Fourier Transform (FFT) analysis. The harmonic content for this fault current, through the loading range, is studied, and fed to neural network algorithm. A numerical optimization technique using Levenberg-Marquardt algorithm has been done for ANN training and testing.This system prepared to be used in industrial applications to diagnose and isolate the faulty motors immediately at their incipient stage, and to avoid any damage occur for the motors, or for their supply system.

التنزيلات

Key Dates

منشور

2012-09-01

إصدار

مجلد 16 عدد 3 (2012): Journal of Engineering and Development

القسم

Articles

الرخصة

Creative Commons License

هذا العمل مرخص بموجب Creative Commons Attribution 4.0 International License.

The Journal of Engineering and Sustainable Development is an open-access journal that all contents are free of charge. Articles of this journal are licensed under the terms of the Creative Commons Attribution International Public License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode) that licensees are unrestrictly allowed to search, download, share, distribute, print, or link to the full texts of the articles, crawl them for indexing and reproduce any medium of the articles provided that they give the author(s) proper credits (citation). The journal allows the author(s) to retain the copyright of their published article.
Creative Commons-Attribution (BY)

كيفية الاقتباس

3-phase Induction Motor Bearing Fault Detection and Isolation using MCSA Technique Based on Neural Network Algorithm. (2012). مجلة الهندسة والتنمية المستدامة, 16(3), 175-189. https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/1292

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