Proceedings of the 2016 International Conference on Engineering Science and Management

A Method for Diagnosing the State of Electric Actuator Based on Neural Network

Authors
Hongli Wang, Bing Xu, Fuli Liu, Yuan Zheng
Corresponding Author
Hongli Wang
Available Online August 2016.
DOI
10.2991/esm-16.2016.4How to use a DOI?
Keywords
State diagnosis, electric actuator, Artificial Intelligence (AI), Neural Network (NN)
Abstract

The article, taking neural network (NN) as the tool and electric actuator as study object, proposes a diagnosis method for electric actuator based on self-organization competitive neural network, by which state diagnosis is realized with discrimination capacity of nonlinear dynamic system of neural network as well as comparison of forecast value of the system and measured values of actual parameters. The system incorporated VB6.0 as development tool and SQL Server2000 as backstage database to realize intelligent diagnosis of states with electric actuator.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2016 International Conference on Engineering Science and Management
Series
Advances in Engineering Research
Publication Date
August 2016
ISBN
978-94-6252-218-3
ISSN
2352-5401
DOI
10.2991/esm-16.2016.4How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Hongli Wang
AU  - Bing Xu
AU  - Fuli Liu
AU  - Yuan Zheng
PY  - 2016/08
DA  - 2016/08
TI  - A Method for Diagnosing the State of Electric Actuator Based on Neural Network
BT  - Proceedings of the 2016 International Conference on Engineering Science and Management
PB  - Atlantis Press
SP  - 15
EP  - 17
SN  - 2352-5401
UR  - https://doi.org/10.2991/esm-16.2016.4
DO  - 10.2991/esm-16.2016.4
ID  - Wang2016/08
ER  -