Diagnosis of Sucker Rod Pump based on generating dynamometer cards-Reference-Cited by-同舟云学术

Diagnosis of Sucker Rod Pump based on generating dynamometer cards

Published:2019-05 Issue: Volume:77 Page:76-88
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

Zheng Boyuan^ORCID,Gao Xianwen^ORCID,Li Xiangyu

Funder

National Natural Science Foundation of China

China Scholarship Council

Doctoral Science Research Foundation of Liaoning Province of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Reference29 articles.

1. Sucker Rod Pump Production Optimization Via Intelligent Real Time Surveillance in Joint Operations - Wafra Field Illustrated Through Case Examples;Bahbahani,2016

2. Oil well diagnosis by sensing terminal characteristics of the induction motor;Wilamowski;IEEE Trans. Ind. Electron.,2000

3. Performance Evaluations of the Different Sucker Rod Artificial Lift Systems;Dave,2017

4. Diagnosis of sucker rod pumping based on dynamometer card decomposition and hidden Markov model;Zheng;Trans. Inst. Meas. Control.,2018

5. A New Approach to Diagnosis of Sucker Rod Pump Systems by Analyzing Segments of Downhole Dynamometer Cards;Reges,2015

Cited by 36 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Intelligent diagnosis method of torque-angle dynamometer cards for beam pumping units based on transfer learning;Geoenergy Science and Engineering;2024-10

2. A Hybrid Method for Solving the One-Dimensional Wave Equation of Tapered Sucker-Rod Strings;Axioms;2024-06-20

3. A lightweight convolutional neural network for working condition intelligent diagnosis of pumping units;Engineering Research Express;2024-06-01

4. A Novel Method for Predicting the Behavior of a Sucker Rod Pumping Unit Based on the Polished Rod Velocity;Mathematics;2024-04-25

5. Multisource working condition recognition via nonlinear kernel learning and p-Laplacian manifold learning;Heliyon;2024-03