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Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Sensors (Basel, Switzerland), 2020-10, Vol.20 (19), p.5659 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 by the authors. 2020 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s20195659 ;PMID: 33022966

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Title:
Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM
Author: Cheng, Haibo ; Yu, Haibin ; Zeng, Peng ; Osipov, Evgeny ; Li, Shichao ; Vyatkin, Valeriy
Subjects: Accuracy ; Algorithms ; Artificial intelligence ; Big Data ; convolutional neural network ; Deep learning ; Dependable Communication and Computation Systems ; dynamometer card ; Error correction ; Fault diagnosis ; Feature extraction ; Gas industry ; Identification methods ; Kommunikations- och beräkningssystem ; Learning ; Letter ; Machine learning ; Neural networks ; Oil field equipment ; Oil fields ; Pattern recognition ; Pumping ; Recognition ; sucker-rod pumping system ; support vector machine ; Support vector machines ; transfer learning ; working condition recognition ; Working conditions
Is Part Of: Sensors (Basel, Switzerland), 2020-10, Vol.20 (19), p.5659
Description: 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.
Publisher: Basel: MDPI AG
Language: English
Identifier: ISSN: 1424-8220
EISSN: 1424-8220
DOI: 10.3390/s20195659
PMID: 33022966
Source: GFMER Free Medical Journals
PubMed Central
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

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