Proceedings of 2013 International Conference on Information Science and Computer Applications

Research on Automatic Diagnosis Based on ANN Well Conditions Fault

Authors
ZhengJia Wu, ShaoXiong Huang, YueSheng Luo
Corresponding Author
ZhengJia Wu
Available Online October 2013.
DOI
https://doi.org/10.2991/isca-13.2013.6How to use a DOI?
Keywords
Pump Indicator Diagram; Finite-Difference; Neural Network; Fault Diagnosis
Abstract
This thesis combines the extraction of geometric characteristics and the neural network, which supplies a method that can define the operating conditions relatively accurately through the diagram feature parameter of the rod pumping system pump and avoids setting up and solving complex nonlinear dynamic equation while also achieving the automatic diagnosis functions of the malfunctions of oil wells. According to the analysis of the error results, we identify the accuracy and effectiveness of this model. Besides, we test the neural network which we have just set up by using the real-time data, and the test results indicate the validity of the method in this thesis.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2013 International Conference on Information Science and Computer Applications (ISCA 2013)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2013
ISBN
978-90786-77-85-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/isca-13.2013.6How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - ZhengJia Wu
AU  - ShaoXiong Huang
AU  - YueSheng Luo
PY  - 2013/10
DA  - 2013/10
TI  - Research on Automatic Diagnosis Based on ANN Well Conditions Fault
BT  - 2013 International Conference on Information Science and Computer Applications (ISCA 2013)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/isca-13.2013.6
DO  - https://doi.org/10.2991/isca-13.2013.6
ID  - Wu2013/10
ER  -