International Journal of Emerging Trends & Technology in Computer Science
A Motivation for Recent Innovation & Research
ISSN 2278-6856
www.ijettcs.org

Call for Paper, Published Articles, Indexing Infromation Induction Machine Rotor Faults Diagnostics through Stator Current Using Artificial Neural Network, Authors : Kanika Gupta, Arunpreet Kaur, Devender Kumar , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), www.ijettcs.org
Volume & Issue no: Volume 3, Issue 4, July - August 2014

Title:
Induction Machine Rotor Faults Diagnostics through Stator Current Using Artificial Neural Network
Author Name:
Kanika Gupta, Arunpreet Kaur, Devender Kumar
Abstract:
Abstract - Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economic reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and non- invasive NN based incipient fault detection scheme for small and medium sized induction motors. Faults and failures of induction machines can lead to excessive downtimes and generate large losses in terms of maintenance and lost revenues. This motivates motor monitoring, incipient fault detection and diagnosis. Non-invasive, inexpensive, and reliable fault detection techniques are often preferred by many engineers. In this paper, a feed forward neural network based fault detection system is developed for performing induction motors rotor faults detection and severity evaluation using stator current. From the motor current spectrum analysis and the broken rotor bar specific frequency components knowledge, the rotor fault signature is extracted and monitored by neural network for fault detection and classification. The proposed methodology has been experimentally tested on a 5 HP/1750 rpm induction motor. The obtained results provide a satisfactory level of accuracy. Keywords: Fault diagnosis and identification, Rotor fault, broken bars, MCSA.
Cite this article:
Kanika Gupta, Arunpreet Kaur, Devender Kumar , " Induction Machine Rotor Faults Diagnostics through Stator Current Using Artificial Neural Network " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 3, Issue 4, July - August 2014 , pp. 013-021 , ISSN 2278-6856.
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