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An electrical motor fault detection scheme based on improved genetic algorithm and optimal neural network.

Author(s)
Bin Ren School of Electronic Engineering, Dongguan University of Technology, Dongguan, 523808, Guangdong, China
Huijie Liu School of Electronic Engineering, Dongguan University of Technology, Dongguan, 523808, Guangdong, China
Lei Yang School of Electronic Engineering, Dongguan University of Technology, Dongguan, 523808, Guangdong, China
Lianglun Cheng School of Automation, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
Abstract
With the development of Computer technology, data fusion and artificial intelligence theories, there are many new solutions for motor fault diagnosis. In terms of the motor fault characteristics, integrated diagnosis is implemented to solve this problem in this article, which adopts an improved genetic algorithm and optimizing neural network methods. That diagnosis method can not only reduce the difficulty of fusion system optimization, but also simulate the effect between different faults in coupling fault mode and achieve a precise diagnosis result. The research of the motor fault monitoring and diagnosis technology with this new method has the important theoretical value and engineering practical significance.
Publication Details
Page(s) 273-277
DOI DOI not available
Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 45, Issue: 1, Year: 2012

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