Working Condition Detection of Suck Rod Pumping System via Extreme Learning Machine

DOI

10.2991/cmes-15.2015.120How to use a DOI?

Keywords

Fault Diagnosis; Suck Rod Pumping System; ELM

Abstract

Detecting the working condition of a suck rod pumping system is an important research subject of oil extraction engineering. In the present paper, we claim a research using Extreme Learning Machine (ELM) to handle downhole dynamometer card auto recognition problems in a suck rod pumping system. First of all, we introduce a set of reasonable dynamometer card features which can reflect the characters of the cards. Then, an ELM associated with the features is constructed to recognize faults of the system automatically. The model we proposed is trained and tested by the real data acquired from Yanchang oil fields, China. Finally, we conclude based on the experiment results that ELM model has excellent generalization performance and is applicable to the automatic fault diagnosis of suck rod pumping system.

Copyright

© 2015, 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)

TY  - CONF
AU  - Qian Gao
AU  - Shaobo Sun
AU  - Jianchao Liu
PY  - 2015/04
DA  - 2015/04
TI  - Working Condition Detection of Suck Rod Pumping System via Extreme Learning Machine
BT  - Proceedings of the 2nd International Conference on Civil, Materials and Environmental Sciences
PB  - Atlantis Press
SP  - 434
EP  - 437
SN  - 2352-5401
UR  - https://doi.org/10.2991/cmes-15.2015.120
DO  - 10.2991/cmes-15.2015.120
ID  - Gao2015/04
ER  -