Diagnosis of Stator Faults Severity in Induction Motors Using Two Intelligent Approaches

被引：41
|
作者：
Cunha Palacios, Rodrigo Henrique [1 ]
da Silva, Ivan Nunes [2 ]
Goedtel, Alessandro [1 ]
Godoy, Wagner Fontes [1 ]
Lopes, Tiago Drummond [1 ]
机构：
[1] Fed Technol Univ Parana, Dept Elect & Comp, BR-86300000 Cornelio Procopio, Brazil
[2] Univ Sao Paulo, Dept Elect Engn, Sao Carlos Sch Engn, BR-03178200 Sao Carlos, SP, Brazil
来源：
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2017年 / 13卷 / 04期
基金：
巴西圣保罗研究基金会;
关键词：
Artificial neural network (ANN); multiagent system (MAS); multilayer perceptron (MLP); pattern recognition; stator faults; three-phase induction motor (TIM); NEURAL-NETWORKS; WINDING FAULTS; MACHINES; MODEL;
D O I：
10.1109/TII.2017.2696978
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Three-phase induction motors are the primary means of transformation of electrical energy into mechanical energy in industry, since they are robust and present low cost. However, despite being robust, these machines are subject to electrical or mechanical faults. Thus, identifying a defect in a running motor may decrease the risk of possible damage. This paper proposes an alternative approach to identify defects in the stator of these motors, by analyzing current signals in the time domain. In addition, it presents the determination of the consequent fault severity by means of two proposals: 1) a multiagent system with a classifier behavior; and 2) a neural estimator. The faults observed are related to short circuits between turns in the stator coil of 1% to 10%. Experimental results are observed with motors of different powers, under various adverse situations of electrical feed and a wide range of load conditions on the machine shaft.
引用
收藏
页码：1681 / 1691
页数：11
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