A Topological and Neural Based Technique for Classification of Faults in Induction Machines

被引：0

作者：

Kumar, R. R. ^{[1
,3
]}

Cirrincione, G. ^{[2
,3
]}

Cirrincione, M. ^{[3
]}

Tortella, A. ^{[1
]}

Andriollo, M. ^{[1
]}

机构：

[1] Univ Padua, Dept Elect Engn, Padua, Italy

[2] Univ Picardie Jules Verne, Lab LTI, Amiens, France

[3] Univ South Pacific, Sch Phys & Engn, Suva, Fiji

来源：

2018 21ST INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS (ICEMS) | 2018年

关键词：

fault diagnosis; induction machine; neural networks; pattern recognition; dimensionality reduction; DIAGNOSIS; MOTORS;

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 presents a data driven approach where at first the most significant features of the three phase current signal are analyzed and then a Curvilinear Component based analysis (CCA), which is a nonlinear manifold learning technique, is performed to compress and interpret the feature behaviour. Finally, a multi-layer perceptron network is used to develop a classifier. The effectiveness of the developed model is verified experimentally with data provided on-line and in real-time.

引用

页码：653 / 658

页数：6

共 34 条

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