Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

Application of ANN in Hydraulic Pressure Control Fault Diagnosis System

Authors
Xiaoyu Zhang, Lili Ding
Corresponding Author
Xiaoyu Zhang
Available Online March 2013.
DOI
10.2991/iccsee.2013.646How to use a DOI?
Keywords
ANN, fault diagnosis, fault isolation, Hydraulic pressure control system, feedback
Abstract

The existing hydraulic pressure control fault diagnosis system is effective on fault detection, but the fault isolation capability is bad. In order to improve the capability of the fault isolation, the artificial neural network (ANN) is used in the fault diagnosis system. Aimed at the representative diagnosis of the hydraulic pressure control system, the three layers feedback network is adopted, the basic theory of conjugate gradient BP neural network is explained in detail, and the key techniques are introduced. Five types of typical faults of hydraulic pressure control system can be distinguished easily by it, the faults diagnosis efficiency is higher 30% than ever and the fault diagnosis capability is better 80% than before.

Copyright
© 2013, 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/).

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Volume Title
Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
Series
Advances in Intelligent Systems Research
Publication Date
March 2013
ISBN
978-90-78677-61-1
ISSN
1951-6851
DOI
10.2991/iccsee.2013.646How to use a DOI?
Copyright
© 2013, 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  - Xiaoyu Zhang
AU  - Lili Ding
PY  - 2013/03
DA  - 2013/03
TI  - Application of ANN in Hydraulic Pressure Control Fault Diagnosis System
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2591
EP  - 2594
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.646
DO  - 10.2991/iccsee.2013.646
ID  - Zhang2013/03
ER  -