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 , Fei Lu , Liping Yang , Kan Wang , Lusi Li , Guang Yang , 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.
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.