Unsupervised neural-network-based algorithm for an on-line diagnosis of three-phase induction motor stator fault

被引：145

作者：

Martins, J. F. ^{[1
]}

Pires, V. Fernao ^{[1
]}

Pires, A. J. ^{[1
]}

机构：

[1] Inst Politecn Setubal, Escola Super Tecnol Setubal, Lab Sistemas Elect Ind, P-2914508 Setubal, Portugal

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2007年 / 54卷 / 01期

关键词：

fault diagnosis; Hebbian learning; induction motors; neural networks; unsupervised learning;

D O I：

10.1109/TIE.2006.888790

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, an automatic algorithm based an unsupervised neural network for an on-line diagnostics of three-phase induction motor stator fault is presented. This algorithm uses the alfa-beta stator currents, as input variables. Then, a fully automatic unsupervised method is applied in which a Hebbian-based unsupervised neural network is used to extract the principal components of the stator current data. These main directions are used to decide where the fault occurs and a relationship between the current components is calculated to verify the severity of the fault. One of the characteristics of this method, given its unsupervised nature, is that it does not need a prior identification of the system. The proposed methodology has been experimentally tested on a 1 kW induction motor. The obtained experimental results show the effectiveness of the proposed method.

引用

页码：259 / 264

页数：6

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