<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

Data publikacji: 10 kwi 2013

Zakres stron: 482 - 504

Otrzymano: 14 sty 2013

Przyjęty: 16 mar 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.