Adaptive Dynamic Clone Selection Neural Network Algorithm for Motor Fault Diagnosis

Wu Hongbing; Lou Peihuang; Tang Dunbing

doi:10.21307/ijssis-2017-551

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Adaptive Dynamic Clone Selection Neural Network Algorithm for Motor Fault Diagnosis

International Journal on Smart Sensing and Intelligent Systems

Volume 6 (2013): Issue 2 (January 2013)

By: Wu Hongbing, Lou Peihuang and Tang Dunbing

Open Access

|Apr 2013

Abstract

A fault diagnosis method based on adaptive dynamic clone selection neural network (ADCSNN) is proposed in this paper. In this method the weights of neural network is encoded as the antibody, and the network error is considered as the antigen. The algorithm is then applied to fault detection of motor equipment. The experiments results show that the fault diagnosis method based on ADCS neural network has the capability in escaping local minimum and improving the algorithm speed, this gives better performance.

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Articles in this issue

DOI: https://doi.org/10.21307/ijssis-2017-551 | Journal eISSN: 1178-5608

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Language: English

Page range: 482 - 504

Submitted on: Jan 14, 2013

Accepted on: Mar 16, 2013

Published on: Apr 10, 2013

Published by: Macquarie University, Australia

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

Adaptive,

neural network,

fault diagnosis,

motor,

clone selection algorithm

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2013 Wu Hongbing, Lou Peihuang, Tang Dunbing, published by Macquarie University, Australia
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 6 (2013): Issue 2 (January 2013)