DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Electronics and Computer Science-IJAECS |
 Journal Home Volume Issue |
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Issue |
Volume-4,Issue-3 ( Mar, 2017 ) | |||||||||
Paper Title |
Spectrum Analysis Based Induction Motor Fault Diagnosis: Ann Approach | |||||||||
Author Name |
Nutan Babhale, Anjali Jawadekar | |||||||||
Affilition |
ME 2 nd yr (student) S.S.G.M. College of Engineering Shegaon, Maharashtra-44203, India Department of Electrical Engineering, S.S.G.M. College of Engineering Shegaon, Maharashtra-44203, India | |||||||||
Pages |
43-46 | |||||||||
Abstract |
Induction motors are vulnerable to many faults which results in becoming catastrophic and cause production shutdown, personal injuries and wastage of raw materials. Thus it is important to prevent the faulty conditions at the initial stages so as to avoid any type of failure in the system. This paper is dealing with the rotor bar fault of the induction motor. The possibility of occurrence of rotor bar faults is about 10 % of all total induction motor faults and is caused by the rotor winding. Condition monitoring and fault diagnosis of an induction motor is important in the production line. It can reduce the cost of maintenance and risk of unexpected failures by allowing the early detection of failures. In this paper we show the result of Welch’s periodogram method according to their performance on the broken rotor bar fault detection problem. The results indicate that Welch’s periodogram method has better fault discrimination capability. A feedforward neural network was used for rotor bar fault based on fault features extracted using Welch’s periodogram method. Keywords- Periodogram method, Welch’s periodogram method, artificial neural network, rotor bar fault | |||||||||
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