Fault Diagnosis and Testing of induction machine using Back Propagation Neural Network

被引：0

作者：

Rajeswaran, N. ^{[1
]}

Madhu, T. ^{[2
]}

Kalavathi, M. Surya ^{[3
]}

机构：

[1] JNTUH SNS Coll Technol, Dept ECE, Coimbatore, Tamil Nadu, India

[2] Swarnandhra Inst Engn & Technol, Narasapur, Andhra Pradesh, India

[3] Jawaharlal Nehru Technol Univ, Hyderabad, Andhra Pradesh, India

来源：

PROCEEDINGS OF THE 2012 IEEE INTERNATIONAL POWER MODULATOR AND HIGH VOLTAGE CONFERENCE | 2012年

关键词：

AI; BPN; Fault diagnosis; FPGA;

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：

The recent developments with AI (Artificial Intelligence) are extremely intricate and are useful in a wide range of domestic and industrial applications. In real time environment, operating the induction motor at variable speeds is a severe constraint. The electrical and mechanical faults can impose unacceptable conditions and protective devices are therefore provided to quickly disconnect the motor from grid. In order to ensure that electrical machines receive adequate protection, extensive testing is performed to verify the high quality of assembly. Fault diagnosis and testing of induction machine is attempted under various load conditions and verified by using Field Programmable Gate Array (FPGA). Back Propagation Neural (BPN) Network is used to calculate the error and correct/regulate the induction motor. This technique has resulted in increased speed and improved fault coverage area of the induction machine.

引用

页码：492 / 495

页数：4

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