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![]() ![]() ![]() ![]() | |
Department | 工业控制网络与系统研究室 |
Source Publication | SENSORS
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ISSN | 1424-8220 |
2020 | |
Volume | 20Issue:19Pages:1-15 |
Indexed By | SCI ; EI |
EI Accession number | 20204109318965 |
WOS ID | WOS:000586762000001 |
Contribution Rank | 1 |
Funding Organization | National 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 |
Keyword | working 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 |
WOS Subject | Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
WOS Keyword | FAULT-DIAGNOSIS |
WOS Research Area | Chemistry ; Engineering ; Instruments & Instrumentation |
Funding Project | National Natural Science Foundation of China[61803368] ; China Postdoctoral Science Foundation[2019M661156] ; Liaoning Provincial Natural Science Foundation of China[20180540114] |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.sia.cn/handle/173321/27716 |
Collection | 工业控制网络与系统研究室 |
Corresponding Author | Yu HB(于海斌) |
Affiliation | 1.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,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,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 20.19(2020):1-15. |
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Automatic recognitio(3716KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text |
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