Title :
Multiple fault detection technique for identifying broken rotor bars
Author :
Wangngon, B. ; Ruangsinchaiwanich, S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Naresuan Univ., Phitsanuloke, Thailand
Abstract :
This paper presents multiple fault detection technique for identifying the broken rotor bar condition based on the motor current signature method together with the artificial neural network. The artificial neural network has emerged potentially as an assistant tool for detecting the fault signal of the electrical machine because it is capable of recognizing patterns. Consequently, the proposed multiple fault detection techniques perform acceptably for recognizing the broken rotor bar problem of the induction motor.
Keywords :
bars; fault diagnosis; induction motors; neural nets; power engineering computing; rotors; signal detection; artificial neural network; broken rotor bars; electrical machine; fault signal detection; induction motor; motor current signature method; multiple fault detection technique; Agriculture; Induction motors; Prototypes; Rotors; Universal Serial Bus; Artificial Neural Network; Broken Rotor Bar; Current Signature Method; Fault Detection;
Conference_Titel :
Electrical Machines and Systems (ICEMS), 2013 International Conference on
Conference_Location :
Busan
Print_ISBN :
978-1-4799-1446-3
DOI :
10.1109/ICEMS.2013.6713156