International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Most Trusted Research Journal Since Year 2012

ISSN: 2319-7064



Review Papers | Electrical Engineering | India | Volume 3 Issue 7, July 2014

A Review on Fault Diagnosis of Induction Motor Using Artificial Neural Networks

Kanika Gupta, Arunpreet Kaur

Different alternatives to detect and diagnose faults in induction machines have been proposed and implemented in the last years. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. The characteristics, obtained by this technique, distinguish them from the traditional ones, which, in most cases, need that the machine which is being analysed is not working to do the diagnosis. This paper reviews an artificial neural network (ANN) based technique to identify rotor faults in a three-phase induction motor. The main types of faults considered are broken bar and dynamic eccentricity. At light load, it is difficult to distinguish between healthy and faulty rotors because the characteristic broken rotor bar fault frequencies are very close to the fundamental component and their amplitudes are small in comparison. As a result, detection of the fault and classification of the fault severity under light load is almost impossible. In order to overcome this problem, the detection of rotor faults in induction machines is done by analysing the starting current using a newly developed quantification technique based on artificial neural networks.

Keywords: Fault Diagnosis and Identification, induction motor, artificial neural network, broken bars, rotor faults

Edition: Volume 3 Issue 7, July 2014

Pages: 680 - 684


How to Cite this Article?

Kanika Gupta, Arunpreet Kaur, "A Review on Fault Diagnosis of Induction Motor Using Artificial Neural Networks", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=20141099, Volume 3 Issue 7, July 2014, 680 - 684

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