Automatic recognition of sucker-rod pumping system working conditions using dynamometer cards with transfer learning and svm
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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2020-10-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
15
Series
Sensors (Switzerland), Volume 20, issue 19
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.Description
Keywords
Convolutional neural network, Dynamometer card, Sucker-rod pumping system, Support vector machine, Transfer learning, Working condition recognition
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Citation
Cheng, H, Yu, H, Zeng, P, Osipov, E, Li, S & Vyatkin, V 2020, ' Automatic recognition of sucker-rod pumping system working conditions using dynamometer cards with transfer learning and svm ', Sensors (Switzerland), vol. 20, no. 19, 5659 . https://doi.org/10.3390/s20195659
