Structure information preserving domain adaptation network for fault diagnosis of Sucker Rod Pumping systems

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiaohua Gu , Fei Lu , Liping Yang , Kan Wang , Lusi Li , Guang Yang , Yiling Sun
{"title":"Structure information preserving domain adaptation network for fault diagnosis of Sucker Rod Pumping systems","authors":"Xiaohua Gu ,&nbsp;Fei Lu ,&nbsp;Liping Yang ,&nbsp;Kan Wang ,&nbsp;Lusi Li ,&nbsp;Guang Yang ,&nbsp;Yiling Sun","doi":"10.1016/j.neunet.2025.107392","DOIUrl":null,"url":null,"abstract":"<div><div>Fault diagnosis is of great importance to the reliability and security of Sucker Rod Pumping (SRP) oil production system. With the development of digital oilfield, data-driven deep learning SRP fault diagnosis has become the development trend of oilfield system. However, due to the different working conditions, time periods, and areas, the fault diagnosis models trained from certain SRP data do not consider the statistical discrepancy of different SRP systems, resulting in insufficient generalization. To consider the fault diagnosis and generalization performances of deep models at the same time, this paper proposes a Structure Information Preserving Domain Adaptation Network (SIP-DAN) for SRP fault diagnosis. Different from the usual domain adaptation methods, SIP-DAN divides the source domain data into different subdomains according to the fault categories of the source domain, and then realizes structure information preserving domain adaptation through subdomains alignment of the source domain and the target domain. Due to the lack of fault category information in the target domain, we designed a Classifier Voting Assisted Alignment (CVAA) mechanism. The target domain data are divided into clusters using fuzzy clustering algorithm. Then, fault diagnosis classifier trained in source domain is employed to classify the samples in each cluster, and the majority voting principle is used to assign pseudo-labels to each cluster in the target domain. With these pseudo-labels, source and target subdomains alignment is carried out by optimizing the Local Maximum Mean Discrepancy (LMMD) loss to achieve fine-grained domain adaptation. Experimental results illustrate that the proposed method is better than the existing methods in fault diagnosis of SRP systems.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107392"},"PeriodicalIF":6.0000,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608025002710","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Fault diagnosis is of great importance to the reliability and security of Sucker Rod Pumping (SRP) oil production system. With the development of digital oilfield, data-driven deep learning SRP fault diagnosis has become the development trend of oilfield system. However, due to the different working conditions, time periods, and areas, the fault diagnosis models trained from certain SRP data do not consider the statistical discrepancy of different SRP systems, resulting in insufficient generalization. To consider the fault diagnosis and generalization performances of deep models at the same time, this paper proposes a Structure Information Preserving Domain Adaptation Network (SIP-DAN) for SRP fault diagnosis. Different from the usual domain adaptation methods, SIP-DAN divides the source domain data into different subdomains according to the fault categories of the source domain, and then realizes structure information preserving domain adaptation through subdomains alignment of the source domain and the target domain. Due to the lack of fault category information in the target domain, we designed a Classifier Voting Assisted Alignment (CVAA) mechanism. The target domain data are divided into clusters using fuzzy clustering algorithm. Then, fault diagnosis classifier trained in source domain is employed to classify the samples in each cluster, and the majority voting principle is used to assign pseudo-labels to each cluster in the target domain. With these pseudo-labels, source and target subdomains alignment is carried out by optimizing the Local Maximum Mean Discrepancy (LMMD) loss to achieve fine-grained domain adaptation. Experimental results illustrate that the proposed method is better than the existing methods in fault diagnosis of SRP systems.
求助全文
约1分钟内获得全文 求助全文
来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信