Title :
General Regression Neural Networks as rotor fault detectors of the induction motor
Author :
Kaminski, Marcin ; Kowalski, Czeslaw T. ; Orlowska-Kowalska, Teresa
Author_Institution :
Inst. of Electr. Machines, Drives & Meas., Wroclaw Univ. of Technol., Wroclaw, Poland
Abstract :
This paper presents the application of the General Regression Neural Networks in the diagnostics of the induction motors. The specific fault symptoms of rotor damages included in measured stator current spectrum are proposed as elements of input vectors of GRNN. The structure and training procedure of such neural detector are described. Diagnostic results obtained by the proposed neural detector of rotor faults are demonstrated.
Keywords :
fault diagnosis; induction motor drives; neural nets; power engineering computing; regression analysis; rotors; general regression neural networks; induction motor drives; neural detector; rotor fault detectors; stator current spectrum; Detectors; Electrical fault detection; Fault detection; Frequency; Induction motors; Mathematical model; Neural networks; Rotors; Signal analysis; Testing;
Conference_Titel :
Industrial Technology (ICIT), 2010 IEEE International Conference on
Conference_Location :
Vi a del Mar
Print_ISBN :
978-1-4244-5695-6
Electronic_ISBN :
978-1-4244-5696-3
DOI :
10.1109/ICIT.2010.5472618
