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Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China. University of Chinese Academy of Sciences, Beijing 100049, China.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0003-0069-640x
Vise andre og tillknytning
2020 (engelsk)Inngår i: Sensors, E-ISSN 1424-8220, Vol. 20, nr 19, artikkel-id 5659Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
MDPI, 2020. Vol. 20, nr 19, artikkel-id 5659
Emneord [en]
working condition recognition, sucker-rod pumping system, dynamometer card, convolutional neural network, transfer learning, support vector machine
HSV kategori
Forskningsprogram
Kommunikations- och beräkningssystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-81094DOI: 10.3390/s20195659ISI: 000586762000001PubMedID: 33022966Scopus ID: 2-s2.0-85092060311OAI: oai:DiVA.org:ltu-81094DiVA, id: diva2:1475037
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Validerad;2020;Nivå 2;2020-10-12 (alebob)

Tilgjengelig fra: 2020-10-12 Laget: 2020-10-12 Sist oppdatert: 2022-02-10bibliografisk kontrollert

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Osipov, EvgenyVyatkin, Valeriy

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