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
收藏
页码：492 / 495
页数：4
相关论文
共 50 条
[31]   Online Fault Diagnosis Using Bioinspired Spike Neural Network [J].
Xu, Lie ;
Ji, Daxiong .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2024, 20 (09) :11466-11474
[32]   Fault diagnosis system for a multilevel inverter using a neural network [J].
Khomfoi, S ;
Tolbert, LM .
IECON 2005: THIRTY-FIRST ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-3, 2005, :1455-1460
[33]   Fault Diagnosis for Solar Panels using Convolutional Neural Network [J].
Huang, Zhendong ;
Duan, Shihui ;
Long, Fei ;
Li, Yongjun ;
Zhu, Jin ;
Ling, Qiang .
PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, :153-158
[34]   A study on fault diagnosis of induction motor using neural-wavelet [J].
Shin, Jung-Ho ;
An, Ming-Shou ;
Kang, Dae-Seong .
MUSP '08: MULTIMEDIA SYSTEMS AND SIGNAL PROCESSING, 2008, :210-+
[35]   An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line [J].
Ngaopitakkul, A. ;
Bunjongjit, S. .
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2013, 44 (09) :1745-1761
[36]   Application of fault propagation intensity in fault diagnosis of CNC machine tool [J].
Zhang, Yingzhi ;
Mu, Liming ;
Liu, Jialin ;
Liu, Jintong ;
Tian, Zhifu ;
Zhang, Yilong .
JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS, 2020, 43 (02) :153-161
[37]   Fast fuzzy neural network for fault diagnosis of rotational machine parts using general parameter learning and adaptation [J].
Satoh, S ;
Shaikh, MS ;
Dote, Y .
SMCIA/01: PROCEEDINGS OF THE 2001 IEEE MOUNTAIN WORKSHOP ON SOFT COMPUTING IN INDUSTRIAL APPLICATIONS, 2001, :87-91
[38]   Effective Fault Diagnosis Based on Wavelet and Convolutional Attention Neural Network for Induction Motors [J].
Minh-Quang Tran ;
Liu, Meng-Kun ;
Quoc-Viet Tran ;
Toan-Khoa Nguyen .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
[39]   41 Face Recognition Using Genetic Algorithm and Back Propagation Neural Network [J].
Anam, Sarawat ;
Islam, Md Shohidul ;
Kashem, M. A. ;
Islam, M. N. ;
Islam, M. R. ;
Islam, M. S. .
IMECS 2009: INTERNATIONAL MULTI-CONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS, VOLS I AND II, 2009, :811-+
[40]   Intelligent machine fault diagnosis based on deep transfer convolutional neural network and extreme learning machine [J].
Cen, Jian ;
Chen, Zhihao ;
Wu, Yinbo ;
Yang, Zhuohong .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2023, 237 (09) :2201-2212
12345