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Automatic recognition of sucker-rod pumping system working conditions using dynamometer cards with transfer learning and svm
Cheng HB(程海波)1,2,3,4; Yu HB(于海斌)1,2,3; Zeng P(曾鹏)1,2,3; Osipov, Evgeny5; Li SC(李世超)1,2,3; Vyatkin, Valeriy5,6
Department工业控制网络与系统研究室
Source PublicationSensors (Switzerland)
ISSN1424-8220
2020
Volume20Issue:19Pages:1-15
Indexed ByEI
EI Accession number20204109318965
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China under grant 61533015 ; National Natural Science Foundation of China under Grant 61803368 ; China Postdoctoral Science Foundation under Grant 2019M661156 ; Liaoning Provincial Natural Science Foundation of China under Grant 20180540114
Keywordworking condition recognition sucker-rod pumping system dynamometer card convolutional neural network transfer learning support vector machine
Abstract

Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27716
Collection工业控制网络与系统研究室
Corresponding AuthorYu HB(于海斌)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4.University of Chinese Academy of Sciences, Beijing 100049, China; Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå 97187, Sweden; Department of Electrical Engineering and Automation, Aalto University, Espoo 02150, Finland
5.Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå 97187, Sweden
6.Department of Electrical Engineering and Automation, Aalto University, Espoo 02150, Finland
Recommended Citation
GB/T 7714
Cheng HB,Yu HB,Zeng P,et al. Automatic recognition of sucker-rod pumping system working conditions using dynamometer cards with transfer learning and svm[J]. Sensors (Switzerland),2020,20(19):1-15.
APA Cheng HB,Yu HB,Zeng P,Osipov, Evgeny,Li SC,&Vyatkin, Valeriy.(2020).Automatic recognition of sucker-rod pumping system working conditions using dynamometer cards with transfer learning and svm.Sensors (Switzerland),20(19),1-15.
MLA Cheng HB,et al."Automatic recognition of sucker-rod pumping system working conditions using dynamometer cards with transfer learning and svm".Sensors (Switzerland) 20.19(2020):1-15.
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