<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Adaptive Dynamic Clone Selection Neural Network Algorithm for Motor Fault Diagnosis

Nanjing University of Aeronautics and Astronautics

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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...

Adaptive Dynamic Clone Selection Neural Network Algorithm for Motor Fault Diagnosis

Published Online: Apr 10, 2013

Page range: 482 - 504

Received: Jan 14, 2013

Accepted: Mar 16, 2013

DOI: https://doi.org/10.21307/ijssis-2017-551

© 2013 Wu Hongbing et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.