Rotor Fault Detection of the Converter-Fed Induction Motor using General Regression Neural Networks

被引：0
|
作者：
Kaminski, Marcin [1 ]
Kowalski, Czeslaw T. [1 ]
机构：
[1] Wroclaw Univ Technol, Inst Maszyn Napedow & Pomiarow Elekt, PL-50372 Wroclaw, Poland
来源：
PRZEGLAD ELEKTROTECHNICZNY | 2012年 / 88卷 / 12A期
关键词：
induction motor; rotor bar faults; fault detection; general regression neural networks; DIAGNOSIS; MACHINES; DRIVES;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper deals with the application of the General Regression Neural Networks as the rotor fault detectors of the converter-fed induction motors. The major advantages of GRNN application in the considered task are simplified design process and high quality of data classification. Specific fault symptoms of the rotor damages included in the measured stator current spectrum are proposed as elements of the input vectors of the GRNN-based detector. Diagnostic results obtained by the proposed neural detector of rotor faults are demonstrated.
引用
收藏
页码：71 / 77
页数：7
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