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/).
Cite this article
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 -