Volume & Issue no: Volume 3, Issue 4, July - August 2014
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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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