Severity Estimation of Stator Winding Short-Circuit Faults Using Cubist

被引：0
|
作者：
dos Santos, Tiago [1 ,3 ]
Ferreira, Fernando J. T. E. [2 ]
Pires, Joao Moura [1 ]
Damasio, Carlos Viegas [1 ]
机构：
[1] Univ Nova Lisboa FCT UNL, Dept Comp Sci, NOVA LINCS, Lisbon, Portugal
[2] Univ Coimbra, Dept Elect & Comp Engn, Inst Syst & Robot, Coimbra, Portugal
[3] Altran Portugal, Lisbon, Portugal
来源：
PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2017) | 2017年 / 10423卷
关键词：
Fault diagnosis; Induction motor; Inter-turn short-circuit; Severity estimation; Machine learning; Regression; Cubist;
D O I：
10.1007/978-3-319-65340-2_18
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, an approach to estimate the severity of stator winding short-circuit faults in squirrel-cage induction motors based on the Cubist model is proposed. This is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park's Vector, both for current and voltage. The proposed method presents a systematic comparison between models, as well as an analysis regarding hyper-parameter tunning, where the novelty of the presented work is mainly associated with the application of data-based analysis techniques to estimate the stator winding short-circuit severity in three-phase squirrel-cage induction motors. The developed solution may be used for tele-monitoring of the motor condition and to implement advanced predictive maintenance strategies.
引用
收藏
页码：217 / 228
页数：12
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